Aggregation operations in a distributed database

ABSTRACT

Querying a distributed database including a table sharded into shards distributed to database instances includes receiving a data-query that includes an aggregation clause on a first column and a grouping clause on a second column; obtaining and outputting results data. Obtaining the results data includes receiving, by a query coordinator, intermediate results data; and combining, by the query coordinator, the intermediate results to obtain the results data. Receiving the intermediate results data includes receiving, from a first database instance, first aggregation values indicating, on a per-group basis in accordance with the grouping clause, a respective aggregation value of distinct values of the first column in accordance with the aggregation clause, and receiving, from a second database instance, second aggregation values indicating, on a per-group basis in accordance with the grouping clause, a respective aggregation value of distinct values of the first column in accordance with the aggregation clause.

BACKGROUND

Advances in computer storage and database technology have led toexponential growth of the amount of data being created. Businesses areoverwhelmed by the volume of the data stored in their computer systems.Existing database analytic tools are inefficient, costly to utilize,and/or require substantial configuration and training.

SUMMARY

Disclosed herein are implementations of aggregation operations in adistributed database.

A first aspect is a method for querying a distributed database. Themethod includes receiving a data-query at the distributed database,where the distributed database includes a table, the table includes afirst column and a second column, the table is partitioned into shardsaccording to a sharding criterion, where the sharding criterionindicates the first column such that a first shard includes one or morerows of the table having a first value of the first column and a secondshard omits a row of the table having the first value of the firstcolumn, the shards are distributed to database instances of thedistributed database, and the data-query includes an aggregation clauseon the first column and a grouping clause on the second column. Themethod also includes identifying a query coordinator for processing thedata-query; obtaining results data responsive to the data-query; andoutputting the results data. Obtaining the results data includesreceiving, by the query coordinator and from at least a subset of thedatabase instances of the distributed database, intermediate resultsdata responsive to at least a portion of the data-query; and combining,by the query coordinator, the intermediate results to obtain the resultsdata. Receiving the intermediate results data includes receiving, from afirst database instance for a first shard, first aggregation valuesindicating, on a per-group basis in accordance with the grouping clause,a respective aggregation value of distinct values of the first column inaccordance with the aggregation clause, and receiving, from a seconddatabase instance for a second shard, second aggregation valuesindicating, on a per-group basis in accordance with the grouping clause,a respective aggregation value of distinct values of the first column inaccordance with the aggregation clause.

A second aspect is a method for query planning in a distributed databasethat includes a table that is partitioned into shards that aredistributed to database instances of the distributed database. Themethod includes receiving a data-query at a query coordinator, where thedata-query includes a first “distinct count” clause on a first column ofthe table and a “group by” clause on least a second column of the table,and where the table is partitioned into the shards according to asharding criterion; formulating a query plan; executing the query planto obtain results data; and outputting the results. The query planincludes respective instructions for converting, at at least some of thedatabase instances, distinct values of the first column grouped byvalues of the second column into a count of the distinct values groupedby the values of the second column to obtain respective intermediateresults; instructions for receiving, at the query coordinator, therespective intermediate results from at least a subset of the at leastsome of the database instances; and instructions for concatenating therespective intermediate results using a summing operation to obtain the“distinct count” of the first column grouped by the second column.

A third aspect is an apparatus for querying a distributed database. Theapparatus includes a processor that configured is to extract, from ashard of a table of the distributed database, respective distinct valuesof a first column of the table for each value of a second column of thetable, where the table is partitioned into shards according to asharding criterion, and the shard includes values of the first columnaccording to the sharding criterion such that the values of the firstcolumn according to the sharding criterion are not in any other shard ofthe shards; obtain an intermediate result by determining a respectivenumber of the respective distinct values for the each value of thesecond column of the table; and transmit the intermediate result to aquery coordinator.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

FIG. 1 is a block diagram of an example of a computing device.

FIG. 2 is a block diagram of an example of a computing system.

FIG. 3 is a block diagram of an example of a low-latency databaseanalysis system.

FIG. 4 illustrates an example of a sharding agnostic aggregationoperation.

FIG. 5 illustrates an example of data partitioning according to agrouping column of a data-query for aggregations together with groupingsof data in a distributed database according to implementations of thisdisclosure.

FIG. 6 is a flowchart of an example of querying a distributed databaseaccording to implementations of this disclosure.

FIG. 7A illustrates an example of data partitioning according to anaggregation column of a data-query for aggregations together withgroupings of data in a distributed database according to implementationsof this disclosure.

FIG. 7B illustrates an example of data partitioning according to anaggregation column and a grouping column of a data-query foraggregations together with groupings of data in a distributed databaseaccording to implementations of this disclosure.

FIG. 8 is a flowchart of an example of query planning in a distributeddatabase according to implementations of this disclosure.

FIG. 9A is a diagram of an example of a query plan for a data-query foraggregations together with groupings of data in a distributed databaseaccording to implementations of this disclosure.

FIG. 9B is a flow diagram of an example of a query plan for a data-queryfor aggregations together with groupings of data in a distributeddatabase according to implementations of this disclosure.

FIG. 10 is a flowchart of an example of querying a distributed databaseaccording to implementations of this disclosure.

DETAILED DESCRIPTION

Businesses and other organizations store large amounts of data, such asbusiness records, transaction records, and the like, in data storagesystems, such as relational database systems that store data as records,or rows, having values, or fields, corresponding to respective columnsin tables that can be interrelated using key values. The utility ofindividual record and tables may be limited without substantialcorrelation, interpretation, and analysis. The complexity of these datastructures and the large volumes of data that can be stored thereinlimit the accessibility of the data and require substantial skilledhuman resources to code procedures and tools that allow business usersto access useful data. The tools that are available for accessing thesesystems are limited to outputting data expressly requested by the usersand lack the capability to identify and prioritize data other than thedata expressly requested. Useful data, such as data aggregations,patterns, and statistical anomalies that would not be available insmaller data sets (e.g., 10,000 rows of data), and may not be apparent,may be derivable using the large volume of data (e.g., millions orbillions of rows) stored in complex data storage systems, such asrelational database systems, and may be inaccessible due to thecomplexity and limitations of the data storage systems.

In a distributed database, such as a distributed in-memory database asdescribed herein, a table may be partitioned into shards. The data ofthe sharded table can be low-latency data as described herein. Shardinga table includes distributing the data (e.g., rows) of the sharded tableamongst the shards in such a way that a row of the sharded table isincluded in a shard and is omitted from the other shards. Sharding atable may include distributing the rows of the table amongst the shardsaccording to sharding criteria. Sharding a table may includedistributing the rows of the table to respective shards based on thevalue in the row for a column identified by the sharding criteria. Forexamples, rows of a sharded table having a first value for the columnidentified by the sharding criteria may be included in a first shard andomitted from a second shard and rows of the sharded table having asecond value for the column may be included in the second shard andomitted from the first shard.

The sharding criteria can be derived from one or more columns of thetable. The sharding criteria can be derived from one column of thesharded table, can use more than one column of the table, or can be someother criteria. The sharding criteria may be used to distribute rows ofthe table amongst the available shards. This may include arranging thedistribution of the rows in a manner such that rows with the samesharding criteria value(s) are placed in the same shard where feasiblebased on the number of shards and the variation in the number of rowsper shard that is desired. In some implementations, all the rows of thetable that have the same sharding criteria value(s) may be stored inonly one of the shards. A shard can include more than one value of thesharding criteria.

A table can be sharded into tens, hundreds, or more shards. A shard caninclude, for example, one row or millions of rows of data. The shardscan be distributed to database instances of the distributed database,such as the in-memory database instances described herein. In anexample, the number of shards can be a multiple of the number ofdatabase instances. As such, more than one shard can be distributed to adatabase instance. The database instances may be implemented on variousdifferent computing devices. Some database instances may be implementedon the same computing device.

Sharding can improve data-query performance. For example, a receiveddata-query may be routed to a subset of the database instances forprocessing. The subset of the database instances may be determined basedon the data-query criteria and the sharding criteria. As such, less dataof the sharded table can be evaluated (e.g., considered, looked through,queried, tested, retrieved, etc.) and processing can be spread outamongst more computing devices with, e.g., greater collective processingand memory capabilities. Sharding can also improve query parallelism byhaving multiple database instances executing a data-query in parallelsuch that each database instance considers a subset of the data of thesharded table.

A data-query, as described herein, can be executed (e.g., processed,etc.) by the distributed database of obtain results data. In an example,the results data may be, or may be further processed to be, output, suchas to for display. The data-query may be executed in response to anexplicit or an implicit request for the results data, such as a requestfor data. The request for data can be a request for an insight object, arequest for a pinboard object, data expressing a usage intent, and thelike, as described herein. A data-query, or a portion thereof, may be anexpression of a request for data that includes aggregating and groupingthe data.

To illustrate, data expressing a usage intent, such as data representinguser input, can be resolved and transformed, or otherwise processed, toobtain a data-query. A data-query may be a representation of the dataexpressing the usage intent, or a portion thereof, expressed inaccordance with a defined structured query language, such as the definedstructured query language of the distributed database. In response todata expressing a usage intent, a data-query may be obtained indicatingaggregations together with groupings. The data expressing a usage intentmay be, for example, “unique count of A by B.” That is, the data-querymay indicate grouping data based on values of a first column andaggregating the data based on a second column. These data expressing ausage intent may be resolved to a defined structured query languageimplemented by the distributed database, and which may be expressed inpseudo-code as “select count (distinct A) from T group by B,” where Aand B are columns of the table T, the column B is referred to as agrouping column, and the column A is referred to as an aggregationcolumn.

Execution of a data-query may include grouping the data in accordancewith a grouping clause. A grouping clause indicates that execution ofthe data-query includes grouping the data based on values of anidentified column (e.g., the grouping column). For example, the groupingclause may be expressed as “group by B” indicating that rows of a tableT may be grouped based on the respective values of column B, wherecolumn B is a column of a table T. Execution of a data-query may includeaggregating the data in accordance with an aggregation clause. Anaggregation clause indicates that execution of the data-query includesaggregating at least a portion of the data based on values of anidentified column (e.g., the aggregation column). For example, theaggregation clause may be expressed as “distinct count A” indicatingthat an aggregation of values of column A from rows of table T may bedetermined, wherein column A is a column of a table T. Execution of adata-query may include a combination of grouping and aggregating basedon a grouping clause and an aggregation clause. For example, in thecombination of a grouping clause and an aggregation clause that isexpressed as “distinct count A group by B,” distinct values of column Amay be aggregated (e.g., counted) on a per-group basis of the column B.

As further described with respect to FIG. 4 , executing a data-querythat includes aggregations together with groupings of data on a shardedtable may include obtaining intermediate results, such as on a per-shardbasis, wherein the intermediate results obtained from a shard mayinclude unique values of the aggregation column on a per-group basiswherein the groups are determined based on the corresponding values ofthe grouping column; transmitting the intermediate results including theunique values of the aggregation column to a query coordinator; andcombining (e.g., unioning) the per-shard intermediate results at thequery coordinator to remove duplicates. This may be performed becausethe various shards may have the same value in the column to which thedistinct count is being applied and thus if the distinct count isperformed on each shard and is then summed together, certain values maybe double-counted as unique values. In other words, the unique countwill have been performed based on the count of unique values per shardinstead of the count of unique values for the query as a whole acrossthe entire data set.

Execution of a data-query that includes transmitting and combining theintermediate results including the unique values may include relativelyhigh resource utilization degrading the performance of the distributeddatabase and the low-latency data analysis system and may cause someoperations to fail due to resource exhaustion. The possibility fordegraded performance and increased usage of the database instancecoordinating the query across shards may also include substantiallyincreased investment in processing, memory, and storage resources forthat coordinating database instance and may also result in increasedenergy expenditures (needed to operate those increased processing,memory, and storage resources, and for the network transmission of theintermediate data) and associated emissions that may result from thegeneration of that energy.

By leveraging sharding criteria, implementations according to thisdisclosure can optimize data-queries for aggregations together withgroupings. When the sharding criteria include at least the aggregationcolumn (e.g., the column A of the above data-query), the aggregationoperation (e.g., counting or determining the cardinality of the distinctvalues) of the aggregation column can be performed at each databaseinstance and the counts (e.g., cardinalities) can be transferred to thequery coordinator. The query coordinator can then summarize (e.g., sum)the received aggregations (e.g., counts, cardinalities) to obtain theresults data.

Implementations including aggregation operations in a distributeddatabase, as described herein, may reduce memory and network utilizationand result in faster execution of data-queries. Aggregation operationsin a distributed database may include receiving a data-query thatincludes an aggregation clause on a first column and a grouping clauseon a second column of a table of a distributed database, where the tableis sharded on at least the first column; obtaining intermediate resultsdata from database instances, where an intermediate result received froma database instance for a shard includes a respective aggregate value ofthe first column for each value of second column available in the shard;and combining the intermediate results by aggregating the respectiveaggregate values from each intermediate result.

As such, the transfer of the unique values of the grouping column fromeach database instance can be avoided; the aggregation operation can bepushed down to the database instance on which the shard is stored suchthat each database instance aggregates a subset of the data; and theunion operation can be eliminated and replaced by a simpler operation(e.g., a sum operation of the separate unique counts for each shard).

Experiments have shown that data-query execution time, memory footprint,and network traffic can be significantly reduced when data-queries foraggregations together with groupings are optimized as described herein.The network traffic can be reduced since database instances need nottransmit the unique values of the aggregation column that is included inthe sharding criteria.

In a comparative example, a data-query on a sharded table was executedusing sharding-agnostic data-query execution and using data-queries foraggregations together with groupings. The table is sharded into threeregions (i.e., shards) where one of the shards is distributed to adatabase instance that is also the query coordinator. The data-queryincludes aggregation clauses on a first, a second, and a third column ofthe sharded table, and includes a grouping clause on a fourth column ofthe table. The table includes 338 million rows and is sharded on thethird column. The first column has a total of 131 unique values, thesecond column has a total of 15 unique values, the third column has atotal of 335 million unique values, and the fourth column (i.e., thegrouping column) has a total of 491 unique values. When the data-querywas executed using the sharding-agnostic data-query execution, the queryexecuted in 95 seconds, used 35 GB of memory across database instances,and resulted in a transfer of 5 GB of data over the network from theother two database instances to the query coordinator. Contrastingly,when data-queries for aggregations together with groupings as describedherein was used, the data-query executed in 9 seconds, used 4 GB ofmemory across database instances, and resulted in a transfer of 341 KBof data over the network from the other two database instances to thequery coordinator.

FIG. 1 is a block diagram of an example of a computing device 1000. Oneor more aspects of this disclosure may be implemented using thecomputing device 1000. The computing device 1000 includes a processor1100, static memory 1200, low-latency memory 1300, an electroniccommunication unit 1400, a user interface 1500, a bus 1600, and a powersource 1700. Although shown as a single unit, any one or more element ofthe computing device 1000 may be integrated into any number of separatephysical units. For example, the low-latency memory 1300 and theprocessor 1100 may be integrated in a first physical unit and the userinterface 1500 may be integrated in a second physical unit. Although notshown in FIG. 1 , the computing device 1000 may include other aspects,such as an enclosure or one or more sensors.

The computing device 1000 may be a stationary computing device, such asa personal computer (PC), a server, a workstation, a minicomputer, or amainframe computer; or a mobile computing device, such as a mobiletelephone, a personal digital assistant (PDA), a laptop, or a tablet PC.

The processor 1100 may include any device or combination of devicescapable of manipulating or processing a signal or other information,including optical processors, quantum processors, molecular processors,or a combination thereof. The processor 1100 may be a central processingunit (CPU), such as a microprocessor, and may include one or moreprocessing units, which may respectively include one or more processingcores. The processor 1100 may include multiple interconnectedprocessors. For example, the multiple processors may be hardwired ornetworked, including wirelessly networked. In some implementations, theoperations of the processor 1100 may be distributed across multiplephysical devices or units that may be coupled directly or across anetwork. In some implementations, the processor 1100 may include acache, or cache memory, for internal storage of operating data orinstructions. The processor 1100 may include one or more special purposeprocessors, one or more digital signal processor (DSP), one or moremicroprocessors, one or more controllers, one or more microcontrollers,one or more integrated circuits, one or more an Application SpecificIntegrated Circuits, one or more Field Programmable Gate Array, one ormore programmable logic arrays, one or more programmable logiccontrollers, firmware, one or more state machines, or any combinationthereof.

The processor 1100 may be operatively coupled with the static memory1200, the low-latency memory 1300, the electronic communication unit1400, the user interface 1500, the bus 1600, the power source 1700, orany combination thereof. The processor may execute, which may includecontrolling, such as by sending electronic signals to, receivingelectronic signals from, or both, the static memory 1200, thelow-latency memory 1300, the electronic communication unit 1400, theuser interface 1500, the bus 1600, the power source 1700, or anycombination thereof to execute, instructions, programs, code,applications, or the like, which may include executing one or moreaspects of an operating system, and which may include executing one ormore instructions to perform one or more aspects described herein, aloneor in combination with one or more other processors.

The static memory 1200 is coupled to the processor 1100 via the bus 1600and may include non-volatile memory, such as a disk drive, or any formof non-volatile memory capable of persistent electronic informationstorage, such as in the absence of an active power supply. Althoughshown as a single block in FIG. 1 , the static memory 1200 may beimplemented as multiple logical or physical units.

The static memory 1200 may store executable instructions or data, suchas application data, an operating system, or a combination thereof, foraccess by the processor 1100. The executable instructions may beorganized into programmable modules or algorithms, functional programs,codes, code segments, or combinations thereof to perform one or moreaspects, features, or elements described herein. The application datamay include, for example, user files, database catalogs, configurationinformation, or a combination thereof. The operating system may be, forexample, a desktop or laptop operating system; an operating system for amobile device, such as a smartphone or tablet device; or an operatingsystem for a large device, such as a mainframe computer.

The low-latency memory 1300 is coupled to the processor 1100 via the bus1600 and may include any storage medium with low-latency data accessincluding, for example, DRAM modules such as DDR SDRAM, Phase-ChangeMemory (PCM), flash memory, or a solid-state drive. Although shown as asingle block in FIG. 1 , the low-latency memory 1300 may be implementedas multiple logical or physical units. Other configurations may be used.For example, low-latency memory 1300, or a portion thereof, andprocessor 1100 may be combined, such as by using a system on a chipdesign.

The low-latency memory 1300 may store executable instructions or data,such as application data for low-latency access by the processor 1100.The executable instructions may include, for example, one or moreapplication programs, that may be executed by the processor 1100. Theexecutable instructions may be organized into programmable modules oralgorithms, functional programs, codes, code segments, and/orcombinations thereof to perform various functions described herein.

The low-latency memory 1300 may be used to store data that is analyzedor processed using the systems or methods described herein. For example,storage of some or all data in low-latency memory 1300 instead of staticmemory 1200 may improve the execution speed of the systems and methodsdescribed herein by permitting access to data more quickly by an orderof magnitude or greater (e.g., nanoseconds instead of microseconds).

The electronic communication unit 1400 is coupled to the processor 1100via the bus 1600. The electronic communication unit 1400 may include oneor more transceivers. The electronic communication unit 1400 may, forexample, provide a connection or link to a network via a networkinterface. The network interface may be a wired network interface, suchas Ethernet, or a wireless network interface. For example, the computingdevice 1000 may communicate with other devices via the electroniccommunication unit 1400 and the network interface using one or morenetwork protocols, such as Ethernet, Transmission ControlProtocol/Internet Protocol (TCP/IP), power line communication (PLC),Wi-Fi, infrared, ultra violet (UV), visible light, fiber optic, wireline, general packet radio service (GPRS), Global System for Mobilecommunications (GSM), code-division multiple access (CDMA), Long-TermEvolution (LTE), or other suitable protocols.

The user interface 1500 may include any unit capable of interfacing witha human user, such as a virtual or physical keypad, a touchpad, adisplay, a touch display, a speaker, a microphone, a video camera, asensor, a printer, or any combination thereof. For example, a keypad canconvert physical input of force applied to a key to an electrical signalthat can be interpreted by computing device 1000. In another example, adisplay can convert electrical signals output by computing device 1000to light. The purpose of such devices may be to permit interaction witha human user, for example by accepting input from the human user andproviding output back to the human user. The user interface 1500 mayinclude a display; a positional input device, such as a mouse, touchpad,touchscreen, or the like; a keyboard; or any other human and machineinterface device. The user interface 1500 may be coupled to theprocessor 1100 via the bus 1600. In some implementations, the userinterface 1500 can include a display, which can be a liquid crystaldisplay (LCD), a cathode-ray tube (CRT), a light emitting diode (LED)display, an organic light emitting diode (OLED) display, an activematrix organic light emitting diode (AMOLED), or other suitable display.In some implementations, the user interface 1500, or a portion thereof,may be part of another computing device (not shown). For example, aphysical user interface, or a portion thereof, may be omitted from thecomputing device 1000 and a remote or virtual interface may be used,such as via the electronic communication unit 1400.

The bus 1600 is coupled to the static memory 1200, the low-latencymemory 1300, the electronic communication unit 1400, the user interface1500, and the power source 1700. Although a single bus is shown in FIG.1 , the bus 1600 may include multiple buses, which may be connected,such as via bridges, controllers, or adapters.

The power source 1700 provides energy to operate the computing device1000. The power source 1700 may be a general-purpose alternating-current(AC) electric power supply, or power supply interface, such as aninterface to a household power source. In some implementations, thepower source 1700 may be a single use battery or a rechargeable batteryto allow the computing device 1000 to operate independently of anexternal power distribution system. For example, the power source 1700may include a wired power source; one or more dry cell batteries, suchas nickel-cadmium (NiCad), nickel-zinc (NiZn), nickel metal hydride(NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any otherdevice capable of powering the computing device 1000.

FIG. 2 is a block diagram of an example of a computing system 2000. Asshown, the computing system 2000 includes an external data sourceportion 2100, an internal database analysis portion 2200, and a systeminterface portion 2300. The computing system 2000 may include otherelements not shown in FIG. 2 , such as computer network elements.

The external data source portion 2100 may be associated with, such ascontrolled by, an external person, entity, or organization(second-party). The internal database analysis portion 2200 may beassociated with, such as created by or controlled by, a person, entity,or organization (first-party). The system interface portion 2300 may beassociated with, such as created by or controlled by, the first-partyand may be accessed by the first-party, the second-party, third-parties,or a combination thereof, such as in accordance with access andauthorization permissions and procedures.

The external data source portion 2100 is shown as including externaldatabase servers 2120 and external application servers 2140. Theexternal data source portion 2100 may include other elements not shownin FIG. 2 . The external data source portion 2100 may include externalcomputing devices, such as the computing device 1000 shown in FIG. 1 ,which may be used by or accessible to the external person, entity, ororganization (second-party) associated with the external data sourceportion 2100, including but not limited to external database servers2120 and external application servers 2140. The external computingdevices may include data regarding the operation of the external person,entity, or organization (second-party) associated with the external datasource portion 2100.

The external database servers 2120 may be one or more computing devicesconfigured to store data in a format and schema determined externallyfrom the internal database analysis portion 2200, such as by asecond-party associated with the external data source portion 2100, or athird party. For example, the external database server 2120 may use arelational database and may include a database catalog with a schema. Insome embodiments, the external database server 2120 may include anon-database data storage structure, such as a text-based datastructure, such as a comma separated variable structure or an extensiblemarkup language formatted structure or file. For example, the externaldatabase servers 2120 can include data regarding the production ofmaterials by the external person, entity, or organization (second-party)associated with the external data source portion 2100, communicationsbetween the external person, entity, or organization (second-party)associated with the external data source portion 2100 and third parties,or a combination thereof. Other data may be included. The externaldatabase may be a structured database system, such as a relationaldatabase operating in a relational database management system (RDBMS),which may be an enterprise database. In some embodiments, the externaldatabase may be an unstructured data source. The external data mayinclude data or content, such as sales data, revenue data, profit data,tax data, shipping data, safety data, sports data, health data, weatherdata, or the like, or any other data, or combination of data, that maybe generated by or associated with a user, an organization, or anenterprise and stored in a database system. For simplicity and clarity,data stored in or received from the external data source portion 2100may be referred to herein as enterprise data.

The external application server 2140 may include application software,such as application software used by the external person, entity, ororganization (second-party) associated with the external data sourceportion 2100. The external application server 2140 may include data ormetadata relating to the application software.

The external database servers 2120, the external application servers2140, or both, shown in FIG. 2 may represent logical units or devicesthat may be implemented on one or more physical units or devices, whichmay be controlled or operated by the first party, the second party, or athird party.

The external data source portion 2100, or aspects thereof, such as theexternal database servers 2120, the external application servers 2140,or both, may communicate with the internal database analysis portion2200, or an aspect thereof, such as one or more of the servers 2220,2240, 2260, and 2280, via an electronic communication medium, which maybe a wired or wireless electronic communication medium. For example, theelectronic communication medium may include a local area network (LAN),a wide area network (WAN), a fiber channel network, the Internet, or acombination thereof.

The internal database analysis portion 2200 is shown as includingservers 2220, 2240, 2260, and 2280. The servers 2220, 2240, 2260, and2280 may be computing devices, such as the computing device 1000 shownin FIG. 1 . Although four servers 2220, 2240, 2260, and 2280 are shownin FIG. 2 , other numbers, or cardinalities, of servers may be used. Forexample, the number of computing devices may be determined based on thecapability of individual computing devices, the amount of data to beprocessed, the complexity of the data to be processed, or a combinationthereof. Other metrics may be used for determining the number ofcomputing devices.

The internal database analysis portion 2200 may store data, processdata, or store and process data. The internal database analysis portion2200 may include a distributed cluster (not expressly shown) which mayinclude two or more of the servers 2220, 2240, 2260, and 2280. Theoperation of distributed cluster, such as the operation of the servers2220, 2240, 2260, and 2280 individually, in combination, or both, may bemanaged by a distributed cluster manager. For example, the server 2220may be the distributed cluster manager. In another example, thedistributed cluster manager may be implemented on another computingdevice (not shown). The data and processing of the distributed clustermay be distributed among the servers 2220, 2240, 2260, and 2280, such asby the distributed cluster manager.

Enterprise data from the external data source portion 2100, such as fromthe external database server 2120, the external application server 2140,or both may be imported into the internal database analysis portion2200. The external database server 2120, the external application server2140, or both may be one or more computing devices and may communicatewith the internal database analysis portion 2200 via electroniccommunication. The imported data may be distributed among, processed by,stored on, or a combination thereof, one or more of the servers 2220,2240, 2260, and 2280. Importing the enterprise data may includeimporting or accessing the data structures of the enterprise data.Importing the enterprise data may include generating internal data,internal data structures, or both, based on the enterprise data. Theinternal data, internal data structures, or both may accuratelyrepresent and may differ from the enterprise data, the data structuresof the enterprise data, or both. In some implementations, enterprisedata from multiple external data sources may be imported into theinternal database analysis portion 2200. For simplicity and clarity,data stored or used in the internal database analysis portion 2200 maybe referred to herein as internal data. For example, the internal data,or a portion thereof, may represent, and may be distinct from,enterprise data imported into or accessed by the internal databaseanalysis portion 2200.

The system interface portion 2300 may include one or more client devices2320, 2340. The client devices 2320, 2340 may be computing devices, suchas the computing device 1000 shown in FIG. 1 . For example, one of theclient devices 2320, 2340 may be a desktop or laptop computer and theother of the client devices 2320, 2340 may be a mobile device,smartphone, or tablet. One or more of the client devices 2320, 2340 mayaccess the internal database analysis portion 2200. For example, theinternal database analysis portion 2200 may provide one or moreservices, application interfaces, or other electronic computercommunication interfaces, such as a web site, and the client devices2320, 2340 may access the interfaces provided by the internal databaseanalysis portion 2200, which may include accessing the internal datastored in the internal database analysis portion 2200.

In an example, one or more of the client devices 2320, 2340 may send amessage or signal indicating a request for data, which may include arequest for data analysis, to the internal database analysis portion2200. The internal database analysis portion 2200 may receive andprocess the request, which may include distributing the processing amongone or more of the servers 2220, 2240, 2260, and 2280, may generate aresponse to the request, which may include generating or modifyinginternal data, internal data structures, or both, and may output theresponse to the client device 2320, 2340 that sent the request.Processing the request may include accessing one or more internal dataindexes, an internal database, or a combination thereof. The clientdevice 2320, 2340 may receive the response, including the response dataor a portion thereof, and may store, output, or both, the response or arepresentation thereof, such as a representation of the response data,or a portion thereof, which may include presenting the representationvia a user interface on a presentation device of the client device 2320,2340, such as to a user of the client device 2320, 2340.

The system interface portion 2300, or aspects thereof, such as one ormore of the client devices 2320, 2340, may communicate with the internaldatabase analysis portion 2200, or an aspect thereof, such as one ormore of the servers 2220, 2240, 2260, and 2280, via an electroniccommunication medium, which may be a wired or wireless electroniccommunication medium. For example, the electronic communication mediummay include a local area network (LAN), a wide area network (WAN), afiber channel network, the Internet, or a combination thereof.

FIG. 3 is a block diagram of an example of a low-latency databaseanalysis system 3000. The low-latency database analysis system 3000, oraspects thereof, may be similar to the internal database analysisportion 2200 shown in FIG. 2 , except as described herein or otherwiseclear from context. The low-latency database analysis system 3000, oraspects thereof, may be implemented on one or more computing devices,such as servers 2220, 2240, 2260, and 2280 shown in FIG. 2 , which maybe in a clustered or distributed computing configuration.

The low-latency database analysis system 3000 may store and maintain theinternal data, or a portion thereof, such as low-latency data, in alow-latency memory device, such as the low-latency memory 1300 shown inFIG. 1 , or any other type of data storage medium or combination of datastorage devices with relatively fast (low-latency) data access,organized in a low-latency data structure. In some embodiments, thelow-latency database analysis system 3000 may be implemented as one ormore logical devices in a cloud-based configuration optimized forautomatic database analysis.

As shown, the low-latency database analysis system 3000 includes adistributed cluster manager 3100, a security and governance unit 3200, adistributed in-memory database 3300, an enterprise data interface unit3400, a distributed in-memory ontology unit 3500, a semantic interfaceunit 3600, a relational search unit 3700, a natural language processingunit 3710, a data utility unit 3720, an insight unit 3730, an objectsearch unit 3800, an object utility unit 3810, a system configurationunit 3820, a user customization unit 3830, a system access interfaceunit 3900, a real-time collaboration unit 3910, a third-partyintegration unit 3920, and a persistent storage unit 3930, which may becollectively referred to as the components of the low-latency databaseanalysis system 3000.

Although not expressly shown in FIG. 3 , one or more of the componentsof the low-latency database analysis system 3000 may be implemented onone or more operatively connected physical or logical computing devices,such as in a distributed cluster computing configuration, such as theinternal database analysis portion 2200 shown in FIG. 2 . Although shownseparately in FIG. 3 , one or more of the components of the low-latencydatabase analysis system 3000, or respective aspects thereof, may becombined or otherwise organized.

The low-latency database analysis system 3000 may include different,fewer, or additional components not shown in FIG. 3 . The aspects orcomponents implemented in an instance of the low-latency databaseanalysis system 3000 may be configurable. For example, the insight unit3730 may be omitted or disabled. One or more of the components of thelow-latency database analysis system 3000 may be implemented in a mannersuch that aspects thereof are divided or combined into variousexecutable modules or libraries in a manner which may differ from thatdescribed herein.

The low-latency database analysis system 3000 may implement anapplication programming interface (API), which may monitor, receive, orboth, input signals or messages from external devices and systems,client systems, process received signals or messages, transmitcorresponding signals or messages to one or more of the components ofthe low-latency database analysis system 3000, and output, such astransmit or send, output messages or signals to respective externaldevices or systems. The low-latency database analysis system 3000 may beimplemented in a distributed computing configuration.

The distributed cluster manager 3100 manages the operative configurationof the low-latency database analysis system 3000. Managing the operativeconfiguration of the low-latency database analysis system 3000 mayinclude controlling the implementation of and distribution of processingand storage across one or more logical devices operating on one or morephysical devices, such as the servers 2220, 2240, 2260, and 2280 shownin FIG. 2 . The distributed cluster manager 3100 may generate andmaintain configuration data for the low-latency database analysis system3000, such as in one or more tables, identifying the operativeconfiguration of the low-latency database analysis system 3000. Forexample, the distributed cluster manager 3100 may automatically updatethe low-latency database analysis system configuration data in responseto an operative configuration event, such as a change in availability orperformance for a physical or logical unit of the low-latency databaseanalysis system 3000. One or more of the component units of low-latencydatabase analysis system 3000 may access the database analysis systemconfiguration data, such as to identify intercommunication parameters orpaths.

The security and governance unit 3200 may describe, implement, enforce,or a combination thereof, rules and procedures for controlling access toaspects of the low-latency database analysis system 3000, such as theinternal data of the low-latency database analysis system 3000 and thefeatures and interfaces of the low-latency database analysis system3000. The security and governance unit 3200 may apply security at anontological level to control or limit access to the internal data of thelow-latency database analysis system 3000, such as to columns, tables,rows, or fields, which may include using row level security.

Although shown as a single unit in FIG. 3 , the distributed in-memorydatabase 3300 may be implemented in a distributed configuration, such asdistributed among the servers 2220, 2240, 2260, and 2280 shown in FIG. 2, which may include multiple in-memory database instances. Eachin-memory database instance may utilize one or more distinct resources,such as processing or low-latency memory resources, that differ from theresources utilized by the other in-memory database instances. In someembodiments, the in-memory database instances may utilize one or moreshared resources, such as resources utilized by two or more in-memorydatabase instances.

The distributed in-memory database 3300 may generate, maintain, or both,a low-latency data structure and data stored or maintained therein(low-latency data). The low-latency data may include principal data,which may represent enterprise data, such as enterprise data importedfrom an external enterprise data source, such as the external datasource portion 2100 shown in FIG. 2 . In some implementations, thedistributed in-memory database 3300 may include system internal datarepresenting one or more aspects, features, or configurations of thelow-latency database analysis system 3000. The distributed in-memorydatabase 3300 and the low-latency data stored therein, or a portionthereof, may be accessed using commands, messages, or signals inaccordance with a defined structured query language associated with thedistributed in-memory database 3300.

The low-latency data, or a portion thereof, may be organized as tablesin the distributed in-memory database 3300. A table may be a datastructure to organize or group the data or a portion thereof, such asrelated or similar data. A table may have a defined structure. Forexample, each table may define or describe a respective set of one ormore columns.

A column may define or describe the characteristics of a discrete aspectof the data in the table. For example, the definition or description ofa column may include an identifier, such as a name, for the columnwithin the table, and one or more constraints, such as a data type, forthe data corresponding to the column in the table. The definition ordescription of a column may include other information, such as adescription of the column. The data in a table may be accessible orpartitionable on a per-column basis. The set of tables, including thecolumn definitions therein, and information describing relationshipsbetween elements, such as tables and columns, of the database may bedefined or described by a database schema or design. The cardinality ofcolumns of a table, and the definition and organization of the columns,may be defined by the database schema or design. Adding, deleting, ormodifying a table, a column, the definition thereof, or a relationshipor constraint thereon, may be a modification of the database design,schema, model, or structure.

The low-latency data, or a portion thereof, may be stored in thedatabase as one or more rows or records in respective tables. Eachrecord or row of a table may include a respective field or cellcorresponding to each column of the table. A field may store a discretedata value. The cardinality of rows of a table, and the values storedtherein, may be variable based on the data. Adding, deleting, ormodifying rows, or the data stored therein may omit modification of thedatabase design, schema, or structure. The data stored in respectivecolumns may be identified or defined as a measure data, attribute data,or enterprise ontology data (e.g., metadata).

Measure data, or measure values, may include quantifiable or additivenumeric values, such as integer or floating-point values, which mayinclude numeric values indicating sizes, amounts, degrees, or the like.A column defined as representing measure values may be referred toherein as a measure or fact. A measure may be a property on whichquantitative operations (e.g., sum, count, average, minimum, maximum)may be performed to calculate or determine a result or output.

Attribute data, or attribute values, may include non-quantifiablevalues, such as text or image data, which may indicate names anddescriptions, quantifiable values designated, defined, or identified asattribute data, such as numeric unit identifiers, or a combinationthereof. A column defined as including attribute values may be referredto herein as an attribute or dimension. For example, attributes mayinclude text, identifiers, timestamps, or the like.

Enterprise ontology data may include data that defines or describes oneor more aspects of the database, such as data that describes one or moreaspects of the attributes, measures, rows, columns, tables,relationships, or other aspects of the data or database schema. Forexample, a portion of the database design, model, or schema may berepresented as enterprise ontology data in one or more tables in thedatabase.

Distinctly identifiable data in the low-latency data may be referred toherein as a data portion. For example, the low-latency data stored inthe distributed in-memory database 3300 may be referred to herein as adata portion, a table from the low-latency data may be referred toherein as a data portion, a column from the low-latency data may bereferred to herein as a data portion, a row or record from thelow-latency data may be referred to herein as a data portion, a valuefrom the low-latency data may be referred to herein as a data portion, arelationship defined in the low-latency data may be referred to hereinas a data portion, enterprise ontology data describing the low-latencydata may be referred to herein as a data portion, or any otherdistinctly identifiable data, or combination thereof, from thelow-latency data may be referred to herein as a data portion.

The distributed in-memory database 3300 may create or add one or moredata portions, such as a table, may read from or access one or more dataportions, may update or modify one or more data portions, may remove ordelete one or more data portions, or a combination thereof. Adding,modifying, or removing data portions may include changes to the datamodel of the low-latency data. Changing the data model of thelow-latency data may include notifying one or more other components ofthe low-latency database analysis system 3000, such as by sending, orotherwise making available, a message or signal indicating the change.For example, the distributed in-memory database 3300 may create or add atable to the low-latency data and may transmit or send a message orsignal indicating the change to the semantic interface unit 3600.

In some implementations, a portion of the low-latency data may representa data model of an external enterprise database and may omit the datastored in the external enterprise database, or a portion thereof. Forexample, prioritized data may be cached in the distributed in-memorydatabase 3300 and the other data may be omitted from storage in thedistributed in-memory database 3300, which may be stored in the externalenterprise database. In some implementations, requesting data from thedistributed in-memory database 3300 may include requesting the data, ora portion thereof, from the external enterprise database.

The distributed in-memory database 3300 may receive one or more messagesor signals indicating respective data-queries for the low-latency data,or a portion thereof, which may include data-queries for modified,generated, or aggregated data generated based on the low-latency data,or a portion thereof. For example, the distributed in-memory database3300 may receive a data-query from the semantic interface unit 3600,such as in accordance with a request for data. The data-queries receivedby the distributed in-memory database 3300 may be agnostic to thedistributed configuration of the distributed in-memory database 3300. Adata-query, or a portion thereof, may be expressed in accordance withthe defined structured query language implemented by the distributedin-memory database 3300. In some implementations, a data-query may beincluded, such as stored or communicated, in a data-query data structureor container.

The distributed in-memory database 3300 may execute or perform one ormore queries to generate or obtain response data responsive to thedata-query based on the low-latency data.

The distributed in-memory database 3300 may interpret, evaluate, orotherwise process a data-query to generate one or moredistributed-queries, which may be expressed in accordance with thedefined structured query language. For example, an in-memory databaseinstance of the distributed in-memory database 3300 may be identified asa query coordinator. The query coordinator may generate a query plan,which may include generating one or more distributed-queries, based onthe received data-query. The query plan may include query executioninstructions for executing one or more queries, or one or more portionsthereof, based on the received data-query by the one or more of thein-memory database instances. Generating the query plan may includeoptimizing the query plan. The query coordinator may distribute, orotherwise make available, the respective portions of the query plan, asquery execution instructions, to the corresponding in-memory databaseinstances.

The respective in-memory database instances may receive thecorresponding query execution instructions from the query coordinator.The respective in-memory database instances may execute thecorresponding query execution instructions to obtain, process, or both,data (intermediate results data) from the low-latency data. Therespective in-memory database instances may output, or otherwise makeavailable, the intermediate results data, such as to the querycoordinator.

The query coordinator may execute a respective portion of queryexecution instructions (allocated to the query coordinator) to obtain,process, or both, data (intermediate results data) from the low-latencydata. The query coordinator may receive, or otherwise access, theintermediate results data from the respective in-memory databaseinstances. The query coordinator may combine, aggregate, or otherwiseprocess, the intermediate results data to obtain results data.

In some embodiments, obtaining the intermediate results data by one ormore of the in-memory database instances may include outputting theintermediate results data to, or obtaining intermediate results datafrom, one or more other in-memory database instances, in addition to, orinstead of, obtaining the intermediate results data from the low-latencydata.

The distributed in-memory database 3300 may output, or otherwise makeavailable, the results data to the semantic interface unit 3600.

The enterprise data interface unit 3400 may interface with, orcommunicate with, an external enterprise data system. For example, theenterprise data interface unit 3400 may receive or access enterprisedata from or in an external system, such as an external database. Theenterprise data interface unit 3400 may import, evaluate, or otherwiseprocess the enterprise data to populate, create, or modify data storedin the low-latency database analysis system 3000. The enterprise datainterface unit 3400 may receive, or otherwise access, the enterprisedata from one or more external data sources, such as the external datasource portion 2100 shown in FIG. 2 , and may represent the enterprisedata in the low-latency database analysis system 3000 by importing,loading, or populating the enterprise data as principal data in thedistributed in-memory database 3300, such as in one or more low-latencydata structures. The enterprise data interface unit 3400 may implementone or more data connectors, which may transfer data between, forexample, the external data source and the distributed in-memory database3300, which may include altering, formatting, evaluating, ormanipulating the data.

The enterprise data interface unit 3400 may receive, access, or generatemetadata that identifies one or more parameters or relationships for theprincipal data, such as based on the enterprise data, and may includethe generated metadata in the low-latency data stored in the distributedin-memory database 3300. For example, the enterprise data interface unit3400 may identify characteristics of the principal data such as,attributes, measures, values, unique identifiers, tags, links, keys, orthe like, and may include metadata representing the identifiedcharacteristics in the low-latency data stored in the distributedin-memory database 3300. The characteristics of the data can beautomatically determined by receiving, accessing, processing,evaluating, or interpreting the schema in which the enterprise data isstored, which may include automatically identifying links orrelationships between columns, classifying columns (e.g., using columnnames), and analyzing or evaluating the data.

Distinctly identifiable operative data units or structures representingone or more data portions, one or more entities, users, groups, ororganizations represented in the internal data, or one or moreaggregations, collections, relations, analytical results,visualizations, or groupings thereof, may be represented in thelow-latency database analysis system 3000 as objects. An object mayinclude a unique identifier for the object, such as a fully qualifiedname. An object may include a name, such as a displayable value, for theobject.

For example, an object may represent a user, a group, an entity, anorganization, a privilege, a role, a table, a column, a datarelationship, a worksheet, a view, a context, an answer, an insight, apinboard, a tag, a comment, a trigger, a defined variable, a datasource, an object-level security rule, a row-level security rule, or anyother data capable of being distinctly identified and stored orotherwise obtained in the low-latency database analysis system 3000. Anobject may represent or correspond with a logical entity. Datadescribing an object may include data operatively or uniquelyidentifying data corresponding to, or represented by, the object in thelow-latency database analysis system. For example, a column in a tablein a database in the low-latency database analysis system may berepresented in the low-latency database analysis system as an object andthe data describing or defining the object may include data operativelyor uniquely identifying the column.

A worksheet (worksheet object), or worksheet table, may be a logicaltable, or a definition thereof, which may be a collection, a sub-set(such as a subset of columns from one or more tables), or both, of datafrom one or more data sources, such as columns in one or more tables,such as in the distributed in-memory database 3300. A worksheet, or adefinition thereof, may include one or more data organization ormanipulation definitions, such as join paths or worksheet-columndefinitions, which may be user defined. A worksheet may be a datastructure that may contain one or more rules or definitions that maydefine or describe how a respective tabular set of data may be obtained,which may include defining one or more sources of data, such as one ormore columns from the distributed in-memory database 3300. A worksheetmay be a data source. For example, a worksheet may include references toone or more data sources, such as columns in one or more tables, such asin the distributed in-memory database 3300, and a request for datareferencing the worksheet may access the data from the data sourcesreferenced in the worksheet. In some implementations, a worksheet mayomit aggregations of the data from the data sources referenced in theworksheet.

An answer (answer object), or report, may be a defined, such aspreviously generated, request for data, such as a resolved-request. Ananswer may include information describing a visualization of dataresponsive to the request for data.

A visualization (visualization object) may be a defined representationor expression of data, such as a visual representation of the data, forpresentation to a user or human observer, such as via a user interface.Although described as a visual representation, in some implementations,a visualization may include non-visual aspects, such as auditory orhaptic presentation aspects. A visualization may be generated torepresent a defined set of data in accordance with a definedvisualization type or template (visualization template object), such asin a chart, graph, or tabular form. Example visualization types mayinclude, and are not limited to, chloropleths, cartograms, dotdistribution maps, proportional symbol maps, contour/isopleth/isarithmicmaps, daysymetric map, self-organizing map, timeline, time series,connected scatter plots, Gantt charts, steam graph/theme river, arcdiagrams, polar area/rose/circumplex charts, Sankey diagrams, alluvialdiagrams, pie charts, histograms, tag clouds, bubble charts, bubbleclouds, bar charts, radial bar charts, tree maps, scatter plots, linecharts, step charts, area charts, stacked graphs, heat maps, parallelcoordinates, spider charts, box and whisker plots, mosaic displays,waterfall charts, funnel charts, or radial tree maps. A visualizationtemplate may define or describe one or more visualization parameters,such as one or more color parameters. Visualization data for avisualization may include values of one or more of the visualizationparameters of the corresponding visualization template.

A view (view object) may be a logical table, or a definition thereof,which may be a collection, a sub-set, or both, of data from one or moredata sources, such as columns in one or more tables, such as in thedistributed in-memory database 3300. For example, a view may begenerated based on an answer, such as by storing the answer as a view. Aview may define or describe a data aggregation. A view may be a datasource. For example, a view may include references to one or more datasources, such as columns in one or more tables, such as in thedistributed in-memory database 3300, which may include a definition ordescription of an aggregation of the data from a respective data source,and a request for data referencing the view may access the aggregateddata, the data from the unaggregated data sources referenced in theworksheet, or a combination thereof. The unaggregated data from datasources referenced in the view defined or described as aggregated datain the view may be unavailable based on the view. A view may be amaterialized view or an unmaterialized view. A request for datareferencing a materialized view may obtain data from a set of datapreviously obtained (view-materialization) in accordance with thedefinition of the view and the request for data. A request for datareferencing an unmaterialized view may obtain data from a set of datacurrently obtained in accordance with the definition of the view and therequest for data.

A pinboard (pinboard object), or dashboard, may be a defined collectionor grouping of objects, such as visualizations, answers, or insights.Pinboard data for a pinboard may include information associated with thepinboard, which may be associated with respective objects included inthe pinboard.

A context (context object) may be a set or collection of data associatedwith a request for data or a discretely related sequence or series ofrequests for data or other interactions with the low-latency databaseanalysis system 3000.

A definition may be a set of data describing the structure ororganization of a data portion. For example, in the distributedin-memory database 3300, a column definition may define one or moreaspects of a column in a table, such as a name of the column, adescription of the column, a datatype for the column, or any otherinformation about the column that may be represented as discrete data.

A data source object may represent a source or repository of dataaccessible by the low-latency database analysis system 3000. A datasource object may include data indicating an electronic communicationlocation, such as an address, of a data source, connection information,such as protocol information, authentication information, or acombination thereof, or any other information about the data source thatmay be represented as discrete data. For example, a data source objectmay represent a table in the distributed in-memory database 3300 andinclude data for accessing the table from the database, such asinformation identifying the database, information identifying a schemawithin the database, and information identifying the table within theschema within the database. An external data source object may representan external data source. For example, an external data source object mayinclude data indicating an electronic communication location, such as anaddress, of an external data source, connection information, such asprotocol information, authentication information, or a combinationthereof, or any other information about the external data source thatmay be represented as discrete data.

A sticker (sticker object) may be a description of a classification,category, tag, subject area, or other information that may be associatedwith one or more other objects such that objects associated with asticker may be grouped, sorted, filtered, or otherwise identified basedon the sticker. In the distributed in-memory database 3300 a tag may bea discrete data portion that may be associated with other data portions,such that data portions associated with a tag may be grouped, sorted,filtered, or otherwise identified based on the tag.

The distributed in-memory ontology unit 3500 generates, maintains, orboth, information (ontological data) defining or describing theoperative ontological structure of the objects represented in thelow-latency database analysis system 3000, such as in the low-latencydata stored in the distributed in-memory database 3300, which mayinclude describing attributes, properties, states, or other informationabout respective objects and may include describing relationships amongrespective objects.

Objects may be referred to herein as primary objects, secondary objects,or tertiary objects. Other types of objects may be used.

Primary objects may include objects representing distinctly identifiableoperative data units or structures representing one or more dataportions in the distributed in-memory database 3300, or another datasource in the low-latency database analysis system 3000. For example,primary objects may be data source objects, table objects, columnobjects, relationship objects, or the like. Primary objects may includeworksheets, views, filters, such as row-level-security filters and tablefilters, variables, or the like. Primary objects may be referred toherein as data-objects or queryable-objects.

Secondary objects may be objects representing distinctly identifiableoperative data units or structures representing analytical dataaggregations, collections, analytical results, visualizations, orgroupings thereof, such as pinboard objects, answer objects, insights,visualization objects, and the like. Secondary objects may be referredto herein as analytical-objects.

Tertiary objects may be objects representing distinctly identifiableoperative data units or structures representing operational aspects ofthe low-latency database analysis system 3000, such as one or moreentities, users, groups, or organizations represented in the internaldata, such as user objects, user-group objects, role objects, stickerobjects, and the like.

The distributed in-memory ontology unit 3500 may represent theontological structure, which may include the objects therein, as a graphhaving nodes and edges. A node may be a representation of an object inthe graph structure of the distributed in-memory ontology unit 3500. Anode, representing an object, can include one or more components. Thecomponents of a node may be versioned, such as on a per-component basis.For example, a node can include a header component, a content component,or both. A header component may include information about the node. Acontent component may include the content of the node. An edge mayrepresent a relationship between nodes, which may be directional.

In some implementations, the distributed in-memory ontology unit 3500graph may include one or more nodes, edges, or both, representing one ormore objects, relationships or both, corresponding to a respectiveinternal representation of enterprise data stored in an externalenterprise data storage unit, wherein a portion of the data stored inthe external enterprise data storage unit represented in the distributedin-memory ontology unit 3500 graph is omitted from the distributedin-memory database 3300.

In some embodiments, the distributed in-memory ontology unit 3500 maygenerate, modify, or remove a portion of the ontology graph in responseto one or more messages, signals, or notifications from one or more ofthe components of the low-latency database analysis system 3000. Forexample, the distributed in-memory ontology unit 3500 may generate,modify, or remove a portion of the ontology graph in response toreceiving one or more messages, signals, or notifications from thedistributed in-memory database 3300 indicating a change to thelow-latency data structure. In another example, the distributedin-memory database 3300 may send one or more messages, signals, ornotifications indicating a change to the low-latency data structure tothe semantic interface unit 3600 and the semantic interface unit 3600may send one or more messages, signals, or notifications indicating thechange to the low-latency data structure to the distributed in-memoryontology unit 3500.

The distributed in-memory ontology unit 3500 may be distributed,in-memory, multi-versioned, transactional, consistent, durable, or acombination thereof. The distributed in-memory ontology unit 3500 istransactional, which may include implementing atomic concurrent, orsubstantially concurrent, updating of multiple objects. The distributedin-memory ontology unit 3500 is durable, which may include implementinga robust storage that prevents data loss subsequent to or as a result ofthe completion of an atomic operation. The distributed in-memoryontology unit 3500 is consistent, which may include performingoperations associated with a request for data with reference to or usinga discrete data set, which may mitigate or eliminate the riskinconsistent results.

The distributed in-memory ontology unit 3500 may generate, output, orboth, one or more event notifications. For example, the distributedin-memory ontology unit 3500 may generate, output, or both, anotification, or notifications, in response to a change of thedistributed in-memory ontology. The distributed in-memory ontology unit3500 may identify a portion of the distributed in-memory ontology(graph) associated with a change of the distributed in-memory ontology,such as one or more nodes depending from a changed node, and maygenerate, output, or both, a notification, or notifications indicatingthe identified relevant portion of the distributed in-memory ontology(graph). One or more aspects of the low-latency database analysis system3000 may cache object data and may receive the notifications from thedistributed in-memory ontology unit 3500, which may reduce latency andnetwork traffic relative to systems that omit caching object data oromit notifications relevant to changes to portions of the distributedin-memory ontology (graph).

The distributed in-memory ontology unit 3500 may implement prefetching.For example, the distributed in-memory ontology unit 3500 maypredictively, such as based on determined probabilistic utility, fetchone or more nodes, such as in response to access to a related node by acomponent of the low-latency database analysis system 3000.

The distributed in-memory ontology unit 3500 may implement amulti-version concurrency control graph data storage unit. Each node,object, or both, may be versioned. Changes to the distributed in-memoryontology may be reversible. For example, the distributed in-memoryontology may have a first state prior to a change to the distributedin-memory ontology, the distributed in-memory ontology may have a secondstate subsequent to the change, and the state of the distributedin-memory ontology may be reverted to the first state subsequent to thechange, such as in response to the identification of an error or failureassociated with the second state.

In some implementations, reverting a node, or a set of nodes, may omitreverting one or more other nodes. In some implementations, thedistributed in-memory ontology unit 3500 may maintain a change logindicating a sequential record of changes to the distributed in-memoryontology (graph), such that a change to a node or a set of nodes may bereverted and one or more other changes subsequent to the reverted changemay be reverted for consistency.

The distributed in-memory ontology unit 3500 may implement optimisticlocking to reduce lock contention times. The use of optimistic lockingpermits improved throughput of data through the distributed in-memoryontology unit 3500.

The semantic interface unit 3600 may implement procedures and functionsto provide a semantic interface between the distributed in-memorydatabase 3300 and one or more of the other components of the low-latencydatabase analysis system 3000.

The semantic interface unit 3600 may implement ontological datamanagement, data-query generation, authentication and access control,object statistical data collection, or a combination thereof.

Ontological data management may include object lifecycle management,object data persistence, ontological modifications, or the like. Objectlifecycle management may include creating one or more objects, readingor otherwise accessing one or more objects, updating or modifying one ormore objects, deleting or removing one or more objects, or a combinationthereof. For example, the semantic interface unit 3600 may interface orcommunicate with the distributed in-memory ontology unit 3500, which maystore the ontological data, object data, or both, to perform objectlifecycle management, object data persistence, ontologicalmodifications, or the like.

For example, the semantic interface unit 3600 may receive, or otherwiseaccess, a message, signal, or notification, such as from the distributedin-memory database 3300, indicating the creation or addition of a dataportion, such as a table, in the low-latency data stored in thedistributed in-memory database 3300, and the semantic interface unit3600 may communicate with the distributed in-memory ontology unit 3500to create an object in the ontology representing the added data portion.The semantic interface unit 3600 may transmit, send, or otherwise makeavailable, a notification, message, or signal to the relational searchunit 3700 indicating that the ontology has changed.

The semantic interface unit 3600 may receive, or otherwise access, arequest message or signal, such as from the relational search unit 3700,indicating a request for information describing changes to the ontology(ontological updates request). The semantic interface unit 3600 maygenerate and send, or otherwise make available, a response message orsignal to the relational search unit 3700 indicating the changes to theontology (ontological updates response). The semantic interface unit3600 may identify one or more data portions for indexing based on thechanges to the ontology. For example, the changes to the ontology mayinclude adding a table to the ontology, the table including multiplerows, and the semantic interface unit 3600 may identify each row as adata portion for indexing. The semantic interface unit 3600 may includeinformation describing the ontological changes in the ontologicalupdates response. The semantic interface unit 3600 may include one ormore data-query definitions, such as data-query definitions for indexingdata-queries, for each data portion identified for indexing in theontological updates response. For example, the data-query definitionsmay include a sampling data-query, which may be used to query thedistributed in-memory database 3300 for sample data from the added dataportion, an indexing data-query, which may be used to query thedistributed in-memory database 3300 for data from the added dataportion, or both.

The semantic interface unit 3600 may receive, or otherwise access,internal signals or messages including data expressing a usage intent,such as data indicating requests to access or modify the low-latencydata stored in the distributed in-memory database 3300 (e.g., a requestfor data). The request to access or modify the low-latency data receivedby the semantic interface unit 3600 may include a resolved-request. Theresolved-request, which may be database and visualization agnostic, maybe expressed or communicated as an ordered sequence of tokens, which mayrepresent semantic data. For example, the relational search unit 3700may tokenize, identify semantics, or both, based on input data, such asinput data representing user input, to generate the resolved-request.The resolved-request may include an ordered sequence of tokens thatrepresent the request for data corresponding to the input data, and maytransmit, send, or otherwise make accessible, the resolved-request tothe semantic interface unit 3600. The semantic interface unit 3600 mayprocess or respond to a received resolved-request.

The semantic interface unit 3600 may process or transform the receivedresolved-request, which may be, at least in part, incompatible with thedistributed in-memory database 3300, to generate one or morecorresponding data-queries that are compatible with the distributedin-memory database 3300, which may include generating a proto-queryrepresenting the resolved-request, generating a pseudo-queryrepresenting the proto-query, and generating the data-query representingthe pseudo-query.

The semantic interface unit 3600 may generate a proto-query based on theresolved-request. A proto-query, which may be database agnostic, may bestructured or formatted in a form, language, or protocol that differsfrom the defined structured query language of the distributed in-memorydatabase 3300. Generating the proto-query may include identifyingvisualization identification data, such as an indication of a type ofvisualization, associated with the request for data, and generating theproto-query based on the resolved-request and the visualizationidentification data.

The semantic interface unit 3600 may transform the proto-query togenerate a pseudo-query. The pseudo-query, which may be databaseagnostic, may be structured or formatted in a form, language, orprotocol that differs from the defined structured query language of thedistributed in-memory database 3300. Generating a pseudo-query mayinclude applying a defined transformation, or an ordered sequence oftransformations. Generating a pseudo-query may include incorporatingrow-level security filters in the pseudo-query.

The semantic interface unit 3600 may generate a data-query based on thepseudo-query, such as by serializing the pseudo-query. The data-query,or a portion thereof, may be structured or formatted using the definedstructured query language of the distributed in-memory database 3300. Insome implementations, a data-query may be structured or formatted usinga defined structured query language of another database, which maydiffer from the defined structured query language of the distributedin-memory database 3300. Generating the data-query may include using oneor more defined rules for expressing respective the structure andcontent of a pseudo-query in the respective defined structured querylanguage.

The semantic interface unit 3600 may communicate, or issue, thedata-query to the distributed in-memory database 3300. In someimplementations, processing or responding to a resolved-request mayinclude generating and issuing multiple data-queries to the distributedin-memory database 3300.

The semantic interface unit 3600 may receive results data from thedistributed in-memory database 3300 responsive to one or moreresolved-requests. The semantic interface unit 3600 may process, format,or transform the results data to obtain visualization data. For example,the semantic interface unit 3600 may identify a visualization forrepresenting or presenting the results data, or a portion thereof, suchas based on the results data or a portion thereof. For example, thesemantic interface unit 3600 may identifying a bar chart visualizationfor results data including one measure and attribute.

Although not shown separately in FIG. 3 , the semantic interface unit3600 may include a data visualization unit. In some embodiments, thedata visualization unit may be a distinct unit, separate from thesemantic interface unit 3600. In some embodiments, the datavisualization unit may be included in the system access interface unit3900. The data visualization unit, the system access interface unit3900, or a combination thereof, may generate a user interface, or one ormore portions thereof. For example, data visualization unit, the systemaccess interface unit 3900, or a combination thereof, may obtain theresults data, such as the visualization data, and may generate userinterface elements (visualizations) representing the results data.

The semantic interface unit 3600 may implement object-level security,row-level security, or a combination thereof. Object level security mayinclude security associated with an object, such as a table, a column, aworksheet, an answer, or a pinboard. Row-level security may includeuser-based or group-based access control of rows of data in thelow-latency data, the indexes, or both. The semantic interface unit 3600may implement on or more authentication procedures, access controlprocedures, or a combination thereof.

The semantic interface unit 3600 may implement one or more user-dataintegration features. For example, the semantic interface unit 3600 maygenerate and output a user interface, or a portion thereof, forinputting, uploading, or importing user data, may receive user data, andmay import the user data. For example, the user data may be enterprisedata.

The semantic interface unit 3600 may implement object statistical datacollection. Object statistical data may include, for respective objects,temporal access information, access frequency information, accessrecency information, access requester information, or the like. Forexample, the semantic interface unit 3600 may obtain object statisticaldata as described with respect to the data utility unit 3720, the objectutility unit 3810, or both. The semantic interface unit 3600 may send,transmit, or otherwise make available, the object statistical data fordata-objects to the data utility unit 3720. The semantic interface unit3600 may send, transmit, or otherwise make available, the objectstatistical data for analytical-objects to the object utility unit 3810.

The semantic interface unit 3600 may implement or expose one or moreservices or application programming interfaces. For example, thesemantic interface unit 3600 may implement one or more services foraccess by the system access interface unit 3900. In someimplementations, one or more services or application programminginterfaces may be exposed to one or more external devices or systems.

The semantic interface unit 3600 may generate and transmit, send, orotherwise communicate, one or more external communications, such ase-mail messages, such as periodically, in response to one or moreevents, or both. For example, the semantic interface unit 3600 maygenerate and transmit, send, or otherwise communicate, one or moreexternal communications including a portable representation, such as aportable document format representation of one or more pinboards inaccordance with a defined schedule, period, or interval. In anotherexample, the semantic interface unit 3600 may generate and transmit,send, or otherwise communicate, one or more external communications inresponse to input data indicating an express request for acommunication. In another example, the semantic interface unit 3600 maygenerate and transmit, send, or otherwise communicate, one or moreexternal communications in response to one or more defined events, suchas the expiration of a recency of access period for a user.

Although shown as a single unit in FIG. 3 , the relational search unit3700 may be implemented in a distributed configuration, which mayinclude a primary relational search unit instance and one or moresecondary relational search unit instances.

The relational search unit 3700 may generate, maintain, operate, or acombination thereof, one or more indexes, such as one or more of anontological index, a constituent data index, a control-word index, anumeral index, or a constant index, based on the low-latency data storedin the distributed in-memory database 3300, the low-latency databaseanalysis system 3000, or both. An index may be a defined data structure,or combination of data structures, for storing tokens, terms, or stringkeys, representing a set of data from one or more defined data sourcesin a form optimized for searching. For example, an index may be acollection of index shards. In some implementations, an index may besegmented into index segments and the index segments may be sharded intoindex shards. In some implementations, an index may be partitioned intoindex partitions, the index partitions may be segmented into indexsegments and the index segments may be sharded into index shards.

Generating, or building, an index may be performed to create or populatea previously unavailable index, which may be referred to as indexing thecorresponding data, and may include regenerating, rebuilding, orreindexing to update or modify a previously available index, such as inresponse to a change in the indexed data (constituent data).

The ontological index may be an index of data (ontological data)describing the ontological structure or schema of the low-latencydatabase analysis system 3000, the low-latency data stored in thedistributed in-memory database 3300, or a combination thereof. Forexample, the ontological index may include data representing the tableand column structure of the distributed in-memory database 3300. Therelational search unit 3700 may generate, maintain, or both, theontological index by communicating with, such as requesting ontologicaldata from, the distributed in-memory ontology unit 3500, the semanticinterface unit 3600, or both. Each record in the ontological index maycorrespond to a respective ontological token, such as a token thatidentifies a column by name.

The control-word index may be an index of a defined set of control-wordtokens. A control-word token may be a character, a symbol, a word, or adefined ordered sequence of characters or symbols, that is identified inone or more grammars of the low-latency database analysis system 3000 ashaving one or more defined grammatical functions, which may becontextual. For example, the control-word index may include thecontrol-word token “sum”, which may be identified in one or moregrammars of the low-latency database analysis system 3000 as indicatingan additive aggregation. In another example, the control-word index mayinclude the control-word token “top”, which may be identified in one ormore grammars of the low-latency database analysis system 3000 asindicating a maximal value from an ordered set. In another example, thecontrol-word index may include operator tokens, such as the equalityoperator token (“=”). The constant index may be an index of constanttokens such as “100” or “true”. The numeral index may be an index ofnumber word tokens (or named numbers), such as number word tokens forthe positive integers between zero and one million, inclusive. Forexample, “one hundred and twenty eight”.

A token may be a word, phrase, character, sequence of characters,symbol, combination of symbols, or the like. A token may represent adata portion in the low-latency data stored in the low-latency datastructure. For example, the relational search unit 3700 mayautomatically generate respective tokens representing the attributes,the measures, the tables, the columns, the values, unique identifiers,tags, links, keys, or any other data portion, or combination of dataportions, or a portion thereof. The relational search unit 3700 mayclassify the tokens, which may include storing token classification datain association with the tokens. For example, a token may be classifiedas an attribute token, a measure token, a value token, or the like.

The constituent data index may be an index of the constituent datavalues stored in the low-latency database analysis system 3000, such asin the distributed in-memory database 3300. The relational search unit3700 may generate, maintain, or both, the constituent data index bycommunicating with, such as requesting data from, the distributedin-memory database 3300. For example, the relational search unit 3700may send, or otherwise communicate, a message or signal to thedistributed in-memory database 3300 indicating a request to perform anindexing data-query, the relational search unit 3700 may receiveresponse data from the distributed in-memory database 3300 in responseto the requested indexing data-query, and the relational search unit3700 may generate the constituent data index, or a portion thereof,based on the response data. For example, the constituent data index mayindex data-objects.

An index shard may be used for token searching, such as exact matchsearching, prefix match searching, substring match searching, or suffixmatch searching. Exact match searching may include identifying tokens inthe index shard that matches a defined target value. Prefix matchsearching may include identifying tokens in the index shard that includea prefix, or begin with a value, such as a character or string, thatmatches a defined target value. Substring match searching may includeidentifying tokens in the index shard that include a value, such as acharacter or string, that matches a defined target value. Suffix matchsearching may include identifying tokens in the index shard that includea suffix, or end with a value, such as a character or string, thatmatches a defined target value. In some implementations, an index shardmay include multiple distinct index data structures. For example, anindex shard may include a first index data structure optimized for exactmatch searching, prefix match searching, and suffix match searching, anda second index data structure optimized for substring match searching.Traversing, or otherwise accessing, managing, or using, an index mayinclude identifying one or more of the index shards of the index andtraversing the respective index shards. In some implementations, one ormore indexes, or index shards, may be distributed, such as replicated onmultiple relational search unit instances. For example, the ontologicalindex may be replicated on each relational search unit instance.

The relational search unit 3700 may receive a request for data from thelow-latency database analysis system 3000. For example, the relationalsearch unit 3700 may receive data expressing a usage intent indicatingthe request for data in response to input, such as user input, obtainedvia a user interface, such as a user interface generated, or partiallygenerated, by the system access interface unit 3900, which may be a userinterface operated on an external device, such as one of the clientdevices 2320, 2340 shown in FIG. 2 . In some implementations, therelational search unit 3700 may receive the data expressing the usageintent from the system access interface unit 3900 or from the semanticinterface unit 3600. For example, the relational search unit 3700 mayreceive or access the data expressing the usage intent in a request fordata message or signal.

The relational search unit 3700 may process, parse, identify semantics,tokenize, or a combination thereof, the request for data to generate aresolved-request, which may include identifying a database andvisualization agnostic ordered sequence of tokens based on the dataexpressing the usage intent. The data expressing the usage intent, orrequest for data, may include request data, such as resolved requestdata, unresolved request data, or a combination of resolved request dataand unresolved request data. The relational search unit 3700 mayidentify the resolved request data. The relational search unit 3700 mayidentify the unresolved request data and may tokenize the unresolvedrequest data.

Resolved request data may be request data identified in the dataexpressing the usage intent as resolved request data. Each resolvedrequest data portion may correspond with a respective token in thelow-latency database analysis system 3000. The data expressing the usageintent may include information identifying one or more portions of therequest data as resolved request data.

Unresolved request data may be request data identified in the dataexpressing the usage intent as unresolved request data, or request datafor which the data expressing the usage intent omits informationidentifying the request data a resolved request data. Unresolved requestdata may include text or string data, which may include a character,sequence of characters, symbol, combination of symbols, word, sequenceof words, phrase, or the like, for which information, such astokenization binding data, identifying the text or string data asresolved request data is absent or omitted from the request data. Thedata expressing the usage intent may include information identifying oneor more portions of the request data as unresolved request data. Thedata expressing the usage intent may omit information identifyingwhether one or more portions of the request data are resolved requestdata. The relational search unit 3700 may identify one or more portionsof the request data for which the data expressing the usage intent omitsinformation identifying whether the one or more portions of the requestdata are resolved request data as unresolved request data.

For example, the data expressing the usage intent may include a requeststring and one or more indications that one or more portions of therequest string are resolved request data. One or more portions of therequest string that are not identified as resolved request data in thedata expressing the usage intent may be identified as unresolved requestdata. For example, the data expressing the usage intent may include therequest string “example text”; the data expressing the usage intent mayinclude information indicating that the first portion of the requeststring, “example”, is resolved request data; and the data expressing theusage intent may omit information indicating that the second portion ofthe request string, “text”, is resolved request data.

The information identifying one or more portions of the request data asresolved request data may include tokenization binding data indicating apreviously identified token corresponding to the respective portion ofthe request data. The tokenization binding data corresponding to arespective token may include, for example, one or more of a columnidentifier indicating a column corresponding to the respective token, adata type identifier corresponding to the respective token, a tableidentifier indicating a table corresponding to the respective token, anindication of an aggregation corresponding to the respective token, oran indication of a join path associated with the respective token. Othertokenization binding data may be used. In some implementations, the dataexpressing the usage intent may omit the tokenization binding data andmay include an identifier that identifies the tokenization binding data.

The relational search unit 3700 may implement or access one or moregrammar-specific tokenizers, such as a tokenizer for a defineddata-analytics grammar or a tokenizer for a natural-language grammar.For example, the relational search unit 3700 may implement one or moreof a formula tokenizer, a row-level-security tokenizer, a relationalsearch tokenizer, or a natural language tokenizer. Other tokenizers maybe used. In some implementations, the relational search unit 3700 mayimplement one or more of the grammar-specific tokenizers, or a portionthereof, by accessing another component of the low-latency databaseanalysis system 3000 that implements the respective grammar-specifictokenizer, or a portion thereof. For example, the natural languageprocessing unit 3710 may implement the natural language tokenizer andthe relational search unit 3700 may access the natural languageprocessing unit 3710 to implement natural language tokenization.

A tokenizer, such as the relational search tokenizer, may parse text orstring data (request string), such as string data included in a dataexpressing the usage intent, in a defined read order, such as from leftto right, such as on a character-by-character or symbol-by-symbol basis.For example, a request string may include a single character, symbol, orletter, and tokenization may include identifying one or more tokensmatching, or partially matching, the input character.

Tokenization may include parsing the request string to identify one ormore words or phrases. For example, the request string may include asequence of characters, symbols, or letters, and tokenization mayinclude parsing the sequence of characters in a defined order, such asfrom left to right, to identify distinct words or terms and identifyingone or more tokens matching the respective words. In someimplementations, word or phrase parsing may be based on one or more of aset of defined delimiters, such as a whitespace character, a punctuationcharacter, or a mathematical operator.

The relational search unit 3700 may traverse one or more of the indexesto identify one or more tokens corresponding to a character, word, orphrase identified in request string. Tokenization may includeidentifying multiple candidate tokens matching a character, word, orphrase identified in request string. Candidate tokens may be ranked orordered, such as based on probabilistic utility.

Tokenization may include match-length maximization. Match-lengthmaximization may include ranking or ordering candidate matching tokensin descending magnitude order. For example, the longest candidate token,having the largest cardinality of characters or symbols, matching therequest string, or a portion thereof, may be the highest rankedcandidate token. For example, the request string may include a sequenceof words or a semantic phrase, and tokenization may include identifyingone or more tokens matching the input semantic phrase. In anotherexample, the request string may include a sequence of phrases, andtokenization may include identifying one or more tokens matching theinput word sequence. In some implementations, tokenization may includeidentifying the highest ranked candidate token for a portion of therequest string as a resolved token for the portion of the requeststring.

The relational search unit 3700 may implement one or more finite statemachines. For example, tokenization may include using one or more finitestate machines. A finite state machine may model or represent a definedset of states and a defined set of transitions between the states. Astate may represent a condition of the system represented by the finitestate machine at a defined temporal point. A finite state machine maytransition from a state (current state) to a subsequent state inresponse to input (e.g., input to the finite state machine). Atransition may define one or more actions or operations that therelational search unit 3700 may implement. One or more of the finitestate machines may be non-deterministic, such that the finite statemachine may transition from a state to zero or more subsequent states.

The relational search unit 3700 may generate, instantiate, or operate atokenization finite state machine, which may represent the respectivetokenization grammar. Generating, instantiating, or operating a finitestate machine may include operating a finite state machine traverser fortraversing the finite state machine. Instantiating the tokenizationfinite state machine may include entering an empty state, indicating theabsence of received input. The relational search unit 3700 may initiateor execute an operation, such as an entry operation, corresponding tothe empty state in response to entering the empty state. Subsequently,the relational search unit 3700 may receive input data, and thetokenization finite state machine may transition from the empty state toa state corresponding to the received input data. In some embodiments,the relational search unit 3700 may initiate one or more data-queries inresponse to transitioning to or from a respective state of a finitestate machine. In the tokenization finite state machine, a state mayrepresent a possible next token in the request string. The tokenizationfinite state machine may transition between states based on one or moredefined transition weights, which may indicate a probability oftransiting from a state to a subsequent state.

The tokenization finite state machine may determine tokenization basedon probabilistic path utility. Probabilistic path utility may rank ororder multiple candidate traversal paths for traversing the tokenizationfinite state machine based on the request string. The candidate pathsmay be ranked or ordered based on one or more defined probabilistic pathutility metrics, which may be evaluated in a defined sequence. Forexample, the tokenization finite state machine may determineprobabilistic path utility by evaluating the weights of the respectivecandidate transition paths, the lengths of the respective candidatetransition paths, or a combination thereof. In some implementations, theweights of the respective candidate transition paths may be evaluatedwith high priority relative to the lengths of the respective candidatetransition paths.

In some implementations, one or more transition paths evaluated by thetokenization finite state machine may include a bound state such thatthe candidate tokens available for tokenization of a portion of therequest string may be limited based on the tokenization of a previouslytokenized portion of the request string.

Tokenization may include matching a portion of the request string to oneor more token types, such as a constant token type, a column name tokentype, a value token type, a control-word token type, a date value tokentype, a string value token type, or any other token type defined by thelow-latency database analysis system 3000. A constant token type may bea fixed, or invariant, token type, such as a numeric value. A columnname token type may correspond with a name of a column in the datamodel. A value token type may correspond with an indexed data value. Acontrol-word token type may correspond with a defined set ofcontrol-words. A date value token type may be similar to a control-wordtoken type and may correspond with a defined set of control-words fordescribing temporal information. A string value token type maycorrespond with an unindexed value.

Token matching may include ordering or weighting candidate token matchesbased on one or more token matching metrics. Token matching metrics mayinclude whether a candidate match is within a defined data scope, suchas a defined set of tables, wherein a candidate match outside thedefined data scope (out-of-scope) may be ordered or weighted lower thana candidate match within the define data scope (in-scope). Tokenmatching metrics may include whether, or the degree to which, acandidate match increases query complexity, such as by spanning multipleroots, wherein a candidate match that increases complexity may beordered or weighted lower than a candidate match that does not increasecomplexity or increases complexity to a lesser extent. Token matchingmetrics may include whether the candidate match is an exact match or apartial match, wherein a candidate match that is a partial may beordered or weighted lower than a candidate match that is an exact match.In some implementations, the cardinality of the set of partial matchesmay be limited to a defined value.

Token matching metrics may include a token score (TokenScore), wherein acandidate match with a relatively low token score may be ordered orweighted lower than a candidate match with a relatively high tokenscore. The token score for a candidate match may be determined based oneor more token scoring metrics. The token scoring metrics may include afinite state machine transition weight metric (FSMScore), wherein aweight of transitioning from a current state of the tokenization finitestate machine to a state indicating a candidate matching token is thefinite state machine transition weight metric. The token scoring metricsmay include a cardinality penalty metric (CardinalityScore), wherein acardinality of values (e.g., unique values) corresponding to thecandidate matching token is used as a penalty metric (inversecardinality), which may reduce the token score. The token scoringmetrics may include an index utility metric (IndexScore), wherein adefined utility value, such as one, associated with an object, such as acolumn wherein the matching token represents the column or a value fromthe column, is the index utility metric. In some implementations, thedefined utility values may be configured, such as in response to userinput, on a per object (e.g., per column) basis. The token scoringmetrics may include a usage metric (UBRScore). The usage metric may bedetermined based on a usage based ranking index, one or more usageranking metrics, or a combination thereof. Determining the usage metric(UBRScore) may include determining a usage boost value (UBRBoost). Thetoken score may be determined based on a defined combination of tokenscoring metrics. For example, determining the token score may beexpressed as the following:TokenScore=FSMScore*(IndexScore+UBRScore*UBRBoost)+Min(CardinalityScore,1).

Token matching may include grouping candidate token matches by matchtype, ranking or ordering on a per-match type basis based on tokenscore, and ranking or ordering the match types. For example, the matchtypes may include a first match type for exact matches (having thehighest match type priority order), a second match type for prefixmatches on ontological data (having a match type priority order lowerthan the first match type), a third match type for substring matches onontological data and prefix matches on data values (having a match typepriority order lower than the second match type), a fourth match typefor substring matches on data values (having a match type priority orderlower than the third match type), and a fifth match type for matchesomitted from the first through fourth match types (having a match typepriority order lower than the fourth match type). Other match types andmatch type orders may be used.

Tokenization may include ambiguity resolution. Ambiguity resolution mayinclude token ambiguity resolution, join-path ambiguity resolution, orboth. In some implementations, ambiguity resolution may ceasetokenization in response to the identification of an automatic ambiguityresolution error or failure.

Token ambiguity may correspond with identifying two or more exactlymatching candidate matching tokens. Token ambiguity resolution may bebased on one or more token ambiguity resolution metrics. The tokenambiguity resolution metrics may include using available previouslyresolved token matching or binding data and token ambiguity may beresolved in favor of available previously resolved token matching orbinding data, other relevant tokens resolved from the request string, orboth. The token ambiguity resolution may include resolving tokenambiguity in favor of integer constants. The token ambiguity resolutionmay include resolving token ambiguity in favor of control-words, such asfor tokens at the end of a request for data, such as last, that are notbeing edited.

Join-path ambiguity may correspond with identifying matching tokenshaving two or more candidate join paths. Join-path ambiguity resolutionmay be based on one or more join-path ambiguity resolution metrics. Thejoin-path ambiguity resolution metrics may include using availablepreviously resolved join-path binding data and join-path ambiguity maybe resolved in favor of available previously resolved join-paths. Thejoin-path ambiguity resolution may include favoring join paths thatinclude in-scope objects over join paths that include out-of-scopeobjects. The join-path ambiguity resolution metrics may include acomplexity minimization metric, which may favor a join path that omitsor avoids increasing complexity over join paths that increasecomplexity, such as a join path that may introduce a chasm trap.

The relational search unit 3700 may identify a resolved-request based onthe request string. The resolved-request, which may be database andvisualization agnostic, may be expressed or communicated as an orderedsequence of tokens representing the request for data indicated by therequest string. The relational search unit 3700 may instantiate, orgenerate, one or more resolved-request objects. For example, therelational search unit 3700 may create or store a resolved-requestobject corresponding to the resolved-request in the distributedin-memory ontology unit 3500. The relational search unit 3700 maytransmit, send, or otherwise make available, the resolved-request to thesemantic interface unit 3600.

In some implementations, the relational search unit 3700 may transmit,send, or otherwise make available, one or more resolved-requests, orportions thereof, to the semantic interface unit 3600 in response tofinite state machine transitions. For example, the relational searchunit 3700 may instantiate a search object in response to a firsttransition of a finite state machine. The relational search unit 3700may include a first search object instruction in the search object inresponse to a second transition of the finite state machine. Therelational search unit 3700 may send the search object including thefirst search object instruction to the semantic interface unit 3600 inresponse to the second transition of the finite state machine. Therelational search unit 3700 may include a second search objectinstruction in the search object in response to a third transition ofthe finite state machine. The relational search unit 3700 may send thesearch object including the search object instruction, or a combinationof the first search object instruction and the second search objectinstruction, to the semantic interface unit 3600 in response to thethird transition of the finite state machine. The search objectinstructions may be represented using any annotation, instruction, text,message, list, pseudo-code, comment, or the like, or any combinationthereof that may be converted, transcoded, or translated into structuredsearch instructions for retrieving data from the low-latency data.

The relational search unit 3700 may provide an interface to permit thecreation of user-defined syntax. For example, a user may associate astring with one or more tokens. Accordingly, when the string is entered,the pre-associated tokens are returned in lieu of searching for tokensto match the input.

The relational search unit 3700 may include a localization unit (notexpressly shown). The localization, globalization, regionalization, orinternationalization, unit may obtain source data expressed inaccordance with a source expressive-form and may output destination datarepresenting the source data, or a portion thereof, and expressed usinga destination expressive-form. The data expressive-forms, such as thesource expressive-form and the destination expressive-form, may includeregional or customary forms of expression, such as numeric expression,temporal expression, currency expression, alphabets, natural-languageelements, measurements, or the like. For example, the sourceexpressive-form may be expressed using a canonical-form, which mayinclude using a natural-language, which may be based on English, and thedestination expressive-form may be expressed using a locale-specificform, which may include using another natural-language, which may be anatural-language that differs from the canonical-language. In anotherexample, the destination expressive-form and the source expressive-formmay be locale-specific expressive-forms and outputting the destinationexpressive-form representation of the source expressive-form data mayinclude obtaining a canonical-form representation of the sourceexpressive-form data and obtaining the destination expressive-formrepresentation based on the canonical-form representation. Although, forsimplicity and clarity, the grammars described herein, such as thedata-analytics grammar and the natural language search grammar, aredescribed with relation to the canonical expressive-form, theimplementation of the respective grammars, or portions thereof,described herein may implement locale-specific expressive-forms. Forexample, the relational search tokenizer may include multiplelocale-specific relational search tokenizers.

The natural language processing unit 3710 may receive input dataincluding a natural language string, such as a natural language stringgenerated in accordance with user input. The natural language string mayrepresent a data request expressed in an unrestricted natural languageform, for which data identified or obtained prior to, or in conjunctionwith, receiving the natural language string by the natural languageprocessing unit 3710 indicating the semantic structure, correlation tothe low-latency database analysis system 3000, or both, for at least aportion of the natural language string is unavailable or incomplete.Although not shown separately in FIG. 3 , in some implementations, thenatural language string may be generated or determined based onprocessing an analog signal, or a digital representation thereof, suchas an audio stream or recording or a video stream or recording, whichmay include using speech-to-text conversion.

The natural language processing unit 3710 may analyze, process, orevaluate the natural language string, or a portion thereof, to generateor determine the semantic structure, correlation to the low-latencydatabase analysis system 3000, or both, for at least a portion of thenatural language string. For example, the natural language processingunit 3710 may identify one or more words or terms in the naturallanguage string and may correlate the identified words to tokens definedin the low-latency database analysis system 3000. In another example,the natural language processing unit 3710 may identify a semanticstructure for the natural language string, or a portion thereof. Inanother example, the natural language processing unit 3710 may identifya probabilistic intent for the natural language string, or a portionthereof, which may correspond to an operative feature of the low-latencydatabase analysis system 3000, such as retrieving data from the internaldata, analyzing data the internal data, or modifying the internal data.

The natural language processing unit 3710 may send, transmit, orotherwise communicate request data indicating the tokens, relationships,semantic data, probabilistic intent, or a combination thereof or one ormore portions thereof, identified based on a natural language string tothe relational search unit 3700.

The data utility unit 3720 may receive, process, and maintainuser-agnostic utility data, such as system configuration data,user-specific utility data, such as utilization data, or bothuser-agnostic and user-specific utility data. The utility data mayindicate whether a data portion, such as a column, a record, an insight,or any other data portion, has high utility or low utility within thesystem, such across all users of the system. For example, the utilitydata may indicate that a defined column is a high-utility column or alow-utility column. The data utility unit 3720 may store the utilitydata, such as using the low-latency data structure. For example, inresponse to a user using, or accessing, a data portion, data utilityunit 3720 may store utility data indicating the usage, or access, eventfor the data portion, which may include incrementing a usage eventcounter associated with the data portion. In some embodiments, the datautility unit 3720 may receive the information indicating the usage, oraccess, event for the data portion from the insight unit 3730, and theusage, or access, event for the data portion may indicate that the usageis associated with an insight.

The data utility unit 3720 may receive a signal, message, or othercommunication, indicating a request for utility information. The requestfor utility information may indicate an object or data portion. The datautility unit 3720 may determine, identify, or obtain utility dataassociated with the identified object or data portion. The data utilityunit 3720 may generate and send utility response data responsive to therequest that may indicate the utility data associated with theidentified object or data portion.

The data utility unit 3720 may generate, maintain, operate, or acombination thereof, one or more indexes, such as one or more of a usage(or utility) index, a resolved-request index, or a phrase index, basedon the low-latency data stored in the distributed in-memory database3300, the low-latency database analysis system 3000, or both.

The insight unit 3730 may automatically identify one or more insights,which may be data other than data expressly requested by a user, andwhich may be identified and prioritized, or both, based on probabilisticutility.

The object search unit 3800 may generate, maintain, operate, or acombination thereof, one or more object-indexes, which may be based onthe analytical-objects represented in the low-latency database analysissystem 3000, or a portion thereof, such as pinboards, answers, andworksheets. An object-index may be a defined data structure, orcombination of data structures, for storing analytical-object data in aform optimized for searching. Although shown as a single unit in FIG. 3, the object search unit 3800 may interface with a distinct, separate,object indexing unit (not expressly shown).

The object search unit 3800 may include an object-index populationinterface, an object-index search interface, or both. The object-indexpopulation interface may obtain and store, load, or populateanalytical-object data, or a portion thereof, in the object-indexes. Theobject-index search interface may efficiently access or retrieveanalytical-object data from the object-indexes such as by searching ortraversing the object-indexes, or one or more portions thereof. In someimplementations, the object-index population interface, or a portionthereof, may be a distinct, independent unit.

The object-index population interface may populate, update, or both theobject-indexes, such as periodically, such as in accordance with adefined temporal period, such as thirty minutes. Populating, orupdating, the object-indexes may include obtaining object indexing datafor indexing the analytical-objects represented in the low-latencydatabase analysis system 3000. For example, the object-index populationinterface may obtain the analytical-object indexing data, such as fromthe distributed in-memory ontology unit 3500. Populating, or updating,the object-indexes may include generating or creating an indexing datastructure representing an object. The indexing data structure forrepresenting an object may differ from the data structure used forrepresenting the object in other components of the low-latency databaseanalysis system 3000, such as in the distributed in-memory ontology unit3500.

The object indexing data for an analytical-object may be a subset of theobject data for the analytical-object. The object indexing data for ananalytical-object may include an object identifier for theanalytical-object uniquely identifying the analytical-object in thelow-latency database analysis system 3000, or in a defined data-domainwithin the low-latency database analysis system 3000. The low-latencydatabase analysis system 3000 may uniquely, unambiguously, distinguishan object from other objects based on the object identifier associatedwith the object. The object indexing data for an analytical-object mayinclude data non-uniquely identifying the object. The low-latencydatabase analysis system 3000 may identify one or moreanalytical-objects based on the non-uniquely identifying data associatedwith the respective objects, or one or more portions thereof. In someimplementations, an object identifier may be an ordered combination ofnon-uniquely identifying object data that, as expressed in the orderedcombination, is uniquely identifying. The low-latency database analysissystem 3000 may enforce the uniqueness of the object identifiers.

Populating, or updating, the object-indexes may include indexing theanalytical-object by including or storing the object indexing data inthe object-indexes. For example, the object indexing data may includedata for an analytical-object, the object-indexes may omit data for theanalytical-object, and the object-index population interface may includeor store the object indexing data in an object-index. In anotherexample, the object indexing data may include data for ananalytical-object, the object-indexes may include data for theanalytical-object, and the object-index population interface may updatethe object indexing data for the analytical-object in the object-indexesin accordance with the object indexing data.

Populating, or updating, the object-indexes may include obtaining objectutility data for the analytical-objects represented in the low-latencydatabase analysis system 3000. For example, the object-index populationinterface may obtain the object utility data, such as from the objectutility unit 3810. The object-index population interface may include theobject utility data in the object-indexes in association with thecorresponding objects.

In some implementations, the object-index population interface mayreceive, obtain, or otherwise access the object utility data from adistinct, independent, object utility data population unit, which mayread, obtain, or otherwise access object utility data from the objectutility unit 3810 and may send, transmit, or otherwise provide, theobject utility data to the object search unit 3800. The object utilitydata population unit may send, transmit, or otherwise provide, theobject utility data to the object search unit 3800 periodically, such asin accordance with a defined temporal period, such as thirty minutes.

The object-index search interface may receive, access, or otherwiseobtain data expressing a usage intent with respect to the low-latencydatabase analysis system 3000, which may represent a request to accessdata in the low-latency database analysis system 3000, which mayrepresent a request to access one or more analytical-objects representedin the low-latency database analysis system 3000. The object-indexsearch interface may generate one or more object-index queries based onthe data expressing the usage intent. The object-index search interfacemay send, transmit, or otherwise make available the object-index queriesto one or more of the object-indexes.

The object-index search interface may receive, obtain, or otherwiseaccess object search results data indicating one or moreanalytical-objects identified by searching or traversing theobject-indexes in accordance with the object-index queries. Theobject-index search interface may sort or rank the object search resultsdata based on probabilistic utility in accordance with the objectutility data for the analytical-objects in the object search resultsdata. In some implementations, the object-index search interface mayinclude one or more object search ranking metrics with the object-indexqueries and may receive the object search results data sorted or rankedbased on probabilistic utility in accordance with the object utilitydata for the objects in the object search results data and in accordancewith the object search ranking metrics.

For example, the data expressing the usage intent may include a useridentifier, and the object search results data may include object searchresults data sorted or ranked based on probabilistic utility for theuser. In another example, the data expressing the usage intent mayinclude a user identifier and one or more search terms, and the objectsearch results data may include object search results data sorted orranked based on probabilistic utility for the user identified bysearching or traversing the object-indexes in accordance with the searchterms.

The object-index search interface may generate and send, transmit, orotherwise make available the sorted or ranked object search results datato another component of the low-latency database analysis system 3000,such as for further processing and display to the user.

The object utility unit 3810 may receive, process, and maintainuser-specific object utility data for objects represented in thelow-latency database analysis system 3000. The user-specific objectutility data may indicate whether an object has high utility or lowutility for the user.

The object utility unit 3810 may store the user-specific object utilitydata, such as on a per-object basis, a per-activity basis, or both. Forexample, in response to data indicating an object access activity, suchas a user using, viewing, or otherwise accessing, an object, the objectutility unit 3810 may store user-specific object utility data indicatingthe object access activity for the object, which may includeincrementing an object access activity counter associated with theobject, which may be a user-specific object access activity counter. Inanother example, in response to data indicating an object storageactivity, such as a user storing an object, the object utility unit 3810may store user-specific object utility data indicating the objectstorage activity for the object, which may include incrementing astorage activity counter associated with the object, which may be auser-specific object storage activity counter. The user-specific objectutility data may include temporal information, such as a temporallocation identifier associated with the object activity. Otherinformation associated with the object activity may be included in theobject utility data.

The object utility unit 3810 may receive a signal, message, or othercommunication, indicating a request for object utility information. Therequest for object utility information may indicate one or more objects,one or more users, one or more activities, temporal information, or acombination thereof. The request for object utility information mayindicate a request for object utility data, object utility counter data,or both.

The object utility unit 3810 may determine, identify, or obtain objectutility data in accordance with the request for object utilityinformation. The object utility unit 3810 may generate and send objectutility response data responsive to the request that may indicate theobject utility data, or a portion thereof, in accordance with therequest for object utility information.

For example, a request for object utility information may indicate auser, an object, temporal information, such as information indicating atemporal span, and an object activity, such as the object accessactivity. The request for object utility information may indicate arequest for object utility counter data. The object utility unit 3810may determine, identify, or obtain object utility counter dataassociated with the user, the object, and the object activity having atemporal location within the temporal span, and the object utility unit3810 may generate and send object utility response data including theidentified object utility counter data.

In some implementations, a request for object utility information mayindicate multiple users, or may omit indicating a user, and the objectutility unit 3810 may identify user-agnostic object utility dataaggregating the user-specific object utility data. In someimplementations, a request for object utility information may indicatemultiple objects, may omit indicating an object, or may indicate anobject type, such as answer, pinboard, or worksheet, and the objectutility unit 3810 may identify the object utility data by aggregatingthe object utility data for multiple objects in accordance with therequest. Other object utility aggregations may be used.

The system configuration unit 3820 implement or apply one or morelow-latency database analysis system configurations to enable, disable,or configure one or more operative features of the low-latency databaseanalysis system 3000. The system configuration unit 3820 may store datarepresenting or defining the one or more low-latency database analysissystem configurations. The system configuration unit 3820 may receivesignals or messages indicating input data, such as input data generatedvia a system access interface, such as a user interface, for accessingor modifying the low-latency database analysis system configurations.The system configuration unit 3820 may generate, modify, delete, orotherwise maintain the low-latency database analysis systemconfigurations, such as in response to the input data. The systemconfiguration unit 3820 may generate or determine output data, and mayoutput the output data, for a system access interface, or a portion orportions thereof, for the low-latency database analysis systemconfigurations, such as for presenting a user interface for thelow-latency database analysis system configurations. Although not shownin FIG. 3 , the system configuration unit 3820 may communicate with arepository, such as an external centralized repository, of low-latencydatabase analysis system configurations; the system configuration unit3820 may receive one or more low-latency database analysis systemconfigurations from the repository, and may control or configure one ormore operative features of the low-latency database analysis system 3000in response to receiving one or more low-latency database analysissystem configurations from the repository.

The user customization unit 3830 may receive, process, and maintainuser-specific utility data, such as user defined configuration data,user defined preference data, or a combination thereof. Theuser-specific utility data may indicate whether a data portion, such asa column, a record, autonomous-analysis data, or any other data portionor object, has high utility or low utility to an identified user. Forexample, the user-specific utility data may indicate that a definedcolumn is a high-utility column or a low-utility column. The usercustomization unit 3830 may store the user-specific utility data, suchas using the low-latency data structure. The user-specific utility datamay include, feedback data, such as feedback indicating user inputexpressly describing or representing the utility of a data portion orobject in response to utilization of the data portion or object, such aspositive feedback indicating high utility or negative feedbackindicating low utility. The user customization unit 3830 may store thefeedback in association with a user identifier. The user customizationunit 3830 may store the feedback in association with the context inwhich feedback was obtained. The user customization data, or a portionthereof, may be stored in an in-memory storage unit of the low-latencydatabase analysis system. In some implementations, the usercustomization data, or a portion thereof, may be stored in thepersistent storage unit 3930.

The system access interface unit 3900 may interface with, or communicatewith, a system access unit (not shown in FIG. 3 ), which may be a clientdevice, a user device, or another external device or system, or acombination thereof, to provide access to the internal data, features ofthe low-latency database analysis system 3000, or a combination thereof.For example, the system access interface unit 3900 may receive signals,message, or other communications representing interactions with theinternal data, such as data expressing a usage intent and may outputresponse messages, signals, or other communications responsive to thereceived requests.

The system access interface unit 3900 may generate data for presenting auser interface, or one or more portions thereof, for the low-latencydatabase analysis system 3000. For example, the system access interfaceunit 3900 may generate instructions for rendering, or otherwisepresenting, the user interface, or one or more portions thereof and maytransmit, or otherwise make available, the instructions for rendering,or otherwise presenting, the user interface, or one or more portionsthereof to the system access unit, for presentation to a user of thesystem access unit. For example, the system access unit may present theuser interface via a web browser or a web application and theinstructions may be in the form of HTML, JavaScript, or the like.

In an example, the system access interface unit 3900 may include asearch field user interface element in the user interface. The searchfield user interface element may be an unstructured search string userinput element or field. The system access unit may display theunstructured search string user input element. The system access unitmay receive input data, such as user input data, corresponding to theunstructured search string user input element. The system access unitmay transmit, or otherwise make available, the unstructured searchstring user input to the system access interface unit 3900. The userinterface may include other user interface elements and the systemaccess unit may transmit, or otherwise make available, other user inputdata to the system access interface unit 3900.

The system access interface unit 3900 may obtain the user input data,such as the unstructured search string, from the system access unit. Thesystem access interface unit 3900 may transmit, or otherwise makeavailable, the user input data to one or more of the other components ofthe low-latency database analysis system 3000.

In some embodiments, the system access interface unit 3900 may obtainthe unstructured search string user input as a sequence of individualcharacters or symbols, and the system access interface unit 3900 maysequentially transmit, or otherwise make available, individual or groupsof characters or symbols of the user input data to one or more of theother components of the low-latency database analysis system 3000.

In some embodiments, system access interface unit 3900 may obtain theunstructured search string user input may as a sequence of individualcharacters or symbols, the system access interface unit 3900 mayaggregate the sequence of individual characters or symbols, and maysequentially transmit, or otherwise make available, a currentaggregation of the received user input data to one or more of the othercomponents of the low-latency database analysis system 3000, in responseto receiving respective characters or symbols from the sequence, such ason a per-character or per-symbol basis.

The real-time collaboration unit 3910 may receive signals or messagesrepresenting input received in accordance with multiple users, ormultiple system access devices, associated with a collaboration contextor session, may output data, such as visualizations, generated ordetermined by the low-latency database analysis system 3000 to multipleusers associated with the collaboration context or session, or both. Thereal-time collaboration unit 3910 may receive signals or messagesrepresenting input received in accordance with one or more usersindicating a request to establish a collaboration context or session,and may generate, maintain, or modify collaboration data representingthe collaboration context or session, such as a collaboration sessionidentifier. The real-time collaboration unit 3910 may receive signals ormessages representing input received in accordance with one or moreusers indicating a request to participate in, or otherwise associatewith, a currently active collaboration context or session, and mayassociate the one or more users with the currently active collaborationcontext or session. In some implementations, the input, output, or both,of the real-time collaboration unit 3910 may include synchronizationdata, such as temporal data, that may be used to maintainsynchronization, with respect to the collaboration context or session,among the low-latency database analysis system 3000 and one or moresystem access devices associated with, or otherwise accessing, thecollaboration context or session.

The third-party integration unit 3920 may include an electroniccommunication interface, such as an application programming interface(API), for interfacing or communicating between an external, such asthird-party, application or system, and the low-latency databaseanalysis system 3000. For example, the third-party integration unit 3920may include an electronic communication interface to transfer databetween the low-latency database analysis system 3000 and one or moreexternal applications or systems, such as by importing data into thelow-latency database analysis system 3000 from the external applicationsor systems or exporting data from the low-latency database analysissystem 3000 to the external applications or systems. For example, thethird-party integration unit 3920 may include an electroniccommunication interface for electronic communication with an externalexchange, transfer, load (ETL) system, which may import data into thelow-latency database analysis system 3000 from an external data sourceor may export data from the low-latency database analysis system 3000 toan external data repository. In another example, the third-partyintegration unit 3920 may include an electronic communication interfacefor electronic communication with external machine learning analysissoftware, which may export data from the low-latency database analysissystem 3000 to the external machine learning analysis software and mayimport data into the low-latency database analysis system 3000 from theexternal machine learning analysis software. The third-party integrationunit 3920 may transfer data independent of, or in conjunction with, thesystem access interface unit 3900, the enterprise data interface unit3400, or both.

The persistent storage unit 3930 may include an interface for storingdata on, accessing data from, or both, one or more persistent datastorage devices or systems. For example, the persistent storage unit3930 may include one or more persistent data storage devices, such asthe static memory 1200 shown in FIG. 1 . Although shown as a single unitin FIG. 3 , the persistent storage unit 3930 may include multiplecomponents, such as in a distributed or clustered configuration. Thepersistent storage unit 3930 may include one or more internalinterfaces, such as electronic communication or application programminginterfaces, for receiving data from, sending data to, or both othercomponents of the low-latency database analysis system 3000. Thepersistent storage unit 3930 may include one or more externalinterfaces, such as electronic communication or application programminginterfaces, for receiving data from, sending data to, or both, one ormore external systems or devices, such as an external persistent storagesystem. For example, the persistent storage unit 3930 may include aninternal interface for obtaining key-value tuple data from othercomponents of the low-latency database analysis system 3000, an externalinterface for sending the key-value tuple data to, or storing thekey-value tuple data on, an external persistent storage system, anexternal interface for obtaining, or otherwise accessing, the key-valuetuple data from the external persistent storage system, and an internalkey-value tuple data for sending, or otherwise making available, thekey-value tuple data to other components of the low-latency databaseanalysis system 3000. In another example, the persistent storage unit3930 may include a first external interface for storing data on, orobtaining data from, a first external persistent storage system, and asecond external interface for storing data on, or obtaining data from, asecond external persistent storage system.

FIG. 4 illustrates an example 4000 of a sharding agnostic aggregationoperation. In the example 4000, data of a table is not partitionedaccording to a grouping column or an aggregation column of a data-querythat is a data-query for aggregations together with groupings of data ina distributed database. The example 4000 illustrates a table 4100 thatincludes the columns ID, A, B, and C. The values of the A, B, and Ccolumns can be of any data types (e.g., integer, character, variablecharacter, date, timestamp, or any other data type). The ID column isused herein as a row identifier. “Row X,” as used herein, should beunderstood to mean “the row having the value of X for the ID column.”For example, “row 18” should be read as “the row having the value 18 forthe ID column.” That is row 18 is the row indicated with numeral 4152.Additionally, the values used for the columns A, B, and C are forillustration purposes and no special semantics should be attached tothem.

The table 4100 is sharded into two shards, a first shard 4200 and asecond shard 4300. In the example 4000, the table 4100 is sharded suchthe first shard 4200 includes all rows having an ID value between 1 and10 (i.e., the rows above a line 4150); and the second shard 4300includes all rows having an ID value between 11 and 20 (i.e., the rowsbelow the line 4150).

To execute a query “select count (distinct A) from T group by B,” whereT is the table 4100, the query can be executed according to thefollowing high level steps.

In a first step, distinct elements of A for each value of B are computedfor (i.e., at) each shard. The distinct elements of A for each value ofB, as computed by a the first database instance using the first shard4200, are shown in a first result 4400; and the distinct elements of Afor each value of B, as computed by a the second database instance usingthe second shard 4300, are shown in a second result 4500. The firstdatabase instance and the second database instance can be said to manage(e.g., hold, etc.) the first shard 4200 and the second shard 4300,respectively. Equivalently, the first shard 4200 and the second shard4300 can be said to be distributed to the first database instance andthe second database instance, respectively.

The first result 4400 illustrates that the first shard 4200 includes,for the value Val_B_1 of B, the distinct value Val_A_2 of A; for thevalue Val_B_2 of B, the distinct values Val_A_1 and Val_A_2 of A; andfor the value Val_B_3 of B, the distinct values Val_A_1 and Val_A_2 ofA. The second result 4500 illustrates that the second shard 4300includes, for the value Val_B_1 of B, the distinct values Val_A_2 andVal_A_3 of A; for the value Val_B_2 of B, the distinct values Val_A_3and Val_A_4 of A; and for the value Val_B_3 of B, the distinct valuesVal_A_2 and Val_A_3 of A. It is noted that the first step does notinclude calculating counts 4410 and 4510 of the distinct values. Thecounts 4410 and 4510 are shown for comparative reasons discussed below.

In a second step, the distinct elements of A for each B are obtainedfrom the database instances at one database instance. The one databaseinstance can be, or can act as, a query coordinator as described herein.The query coordinator can be one of the first database instance or thesecond database instance. The one database instance receives the firstresult 4400 and the second result 4500. That is, the one data instancereceives, in addition to the values of B, the distinct values of A foreach value of B from each of the database instances.

In a third step, a union of the distinct elements for each value of B isperformed by the query coordinator. Unoning (e.g., performing a unionoperation) is necessary to remove the duplicate distinct values of A foreach grouping of B. To illustrate, the first result 4400 includes thedistinct value Val_A_2 of A and the second result 4500 includes thedistinct values Val_A_2 and Val_A_3 for the value Val_B_1 of B. Thus,the first result 4400 and the second result 4500 include redundant data.Unioning removes the redundancies and prevents double counting.

In an fourth step, the size of the set for each B is computed andresults 4600 are obtained.

Without the unioning operation of the third step, the count of distinctvalues A for the value Val_B_1 of B would be 1 (from the counts 4410)+2(from the counts 4510)=3, which is not correct. The correct answer is 2,as shown in results 4600. To perform the union operation, all distinctvalues of A may be transmitted from each database instance to the querycoordinator, which can degrade network performance. Additionally,performing the union operation at the query coordinator degradesperformance of the query coordinator.

FIG. 5 illustrates an example 5000 of data partitioning according to agrouping column of a data-query for aggregations together with groupingsof data in a distributed database according to implementations of thisdisclosure. In the example 5000, the table 4100 of FIG. 4 is sharded oncolumn B, which is a grouping column of a data-query for simultaneousaggregations and groupings. For example, the table 4100 is sharded onthe column B of data-query “select count (distinct A) from T group byB.” The example 5000 includes two shards (i.e., a first shard 5100 and asecond shard 5200). However, as already mentioned, many more shards arepossible.

The sharding criteria of the example 5000 are such that values Val_B_1and Val_B_3 of the column B are in the first shard 5100 and valueVal_B_2 of the column B are in the second shard 5200. That is, rows ofthe table 4100 that include either of the values Val_B_1 or Val_B_3 arein the first shard 5100; and rows of the table 4100 that include thevalue Val_B_3 are in the second shard 5200.

Sharding according to the grouping column can mean that all rows havinga particular value of the sharding column may be found in one and onlyone shard. For example, all rows of the table 4100 having Val_B_2 of thecolumn B are in the second shard 5200 and no rows in the other shards(i.e., the first shard 5100) include a Val_B_2 value of B. As such, andas compared to the steps outlined with respect to FIG. 4 , noduplication of data is possible and unique counts for each value of Bcan be computed at the database instances and no values of A need betransmitted from each database instance to the query coordinator.

As such, to compute a count of distinct values of A for each value of B,a count can be computed in parallel per shard across (e.g., by each of,at each of, etc.) the database instances that include the shards and theresults transmitted to the query coordinator for concatenation. Thus,when a table is sharded on the grouping column, maximum parallelism canbe achieved and a relatively small amount of network communication isrequired.

Results 5300 and results 5400 show the counts obtained from the firstshard 5100 and the second shard 5200 respectively. It is noted thatfirst unique values 5310 and second unique values 5410 are shown forcompleteness and are not transmitted to the query coordinator. The querycoordinator can concatenate the received results 5300 and 5400 to obtainresults 5500. The results 5500 includes the same information (e.g., thesame counts) as the results 4600 of FIG. 4 .

FIG. 6 is a flowchart of an example 6000 of querying a distributeddatabase according to implementations of this disclosure.

The example 6000 may be implemented by a distributed database, such asthe distributed in-memory database 3300 of FIG. 3 . For example, theexample 6000 may be implemented by a database instance, such asin-memory database instance as described herein. The example 6000 can beimplemented by an internal database analysis portion, such as theinternal database analysis portion 2200 of FIG. 2 , or an aspectthereof, such as one or more of the servers 2220, 2240, 2260, and 2280.For example, the example 6000 may be implemented using the computingdevice 1000 of FIG. 1 . The example 6000 can be implemented fully orpartially by a database instance that can be a query coordinator.

At 6100, the example 6000 receives a data-query at the distributeddatabase. The data-query includes an aggregation clause on at least thefirst column of the table and a grouping clause on least the secondcolumn of the table. The distributed database includes a table thatincludes a first column and a second column. The table can bepartitioned into shards according to a sharding criterion and the shardscan be distributed to one or more database instances of the distributeddatabase. In an example, a first database instance includes the firstshard and a second database instance includes the second shard.

A first shard includes one or more rows of the table having a firstvalue of the first column and a second shard omits a row of the tablehaving the first value of the first column. In an example, the shardingcriterion can include only the first column of the table, as illustratedwith respect to FIG. 7A. In an example, the sharding criterion includessharding on the first column (i.e., the aggregation column) followed bysharding on the second column (i.e., the grouping column), asillustrated with respect to FIG. 7B. Other sharding criteria arepossible.

The query pseudo-code “select count (distinct A) from T group by B” isagain used for illustration purposes. However, the disclosure is not solimited and other data-queries or syntaxes are possible.

At 6200, the example 6000 identifies (e.g., selects, designates, etc.) aquery coordinator for processing the data-query. In an example, thequery coordinator can be selected from one of the database instances ona round robin basis. In an example, a query coordinator can beidentified. The query coordinator can be identified (e.g., selected,etc.) based on a current load of the database instances. Other ways ofidentifying the query coordinator are possible. As mentioned, the querycoordinator can generate a query plan for executing the data-query. Thequery plan can include execution instructions. At least some of theexecution instructions may be executed by the query coordinator. Atleast some of the execution instructions may be executed by one or moreother database instances of the distributed database. For example, thequery coordinator can forward at least some of the executioninstructions to other database instances to obtain from at least some ofthe database instances intermediate (e.g., partial) results.

At 6300, the example 6000 obtains results data responsive to thedata-query.

Obtaining the results data is described with respect to FIG. 7A and FIG.7B. Obtaining the results can include the steps 6310-6320.

FIG. 7A illustrates an example 7000 of data partitioning according to anaggregation column of a data-query for aggregations together withgroupings of data in a distributed database according to implementationsof this disclosure. In the example 7000, the table 4100 of FIG. 4 issharded using a sharding criterion according to the aggregation columnof the data-query. That is, the table 4100 is sharded on column A. Twoshards are shown: a first shard 7100 and a second shard 7200. Thesharding criteria is such that rows of the table 4100 having the valuesVal_A_1 and Val_A_3 for column A are in the first shard 7100, and rowsof the table 4100 having the values Val_A_2 and Val_A_4 for column A arein the second shard 7200.

FIG. 7B illustrates an example 7600 of data partitioning according to anaggregation column and a grouping column of a data-query foraggregations together with groupings of data in a distributed databaseaccording to implementations of this disclosure. In the example 7600,the table 4100 of FIG. 4 is sharded using a sharding criterion accordingto the aggregation column (e.g., column A) of the data-query followed bythe grouping column (grouping B) of the data-query. In the example,7600, the table 4100 is partitioned into four shards: shards 7610-7640,which illustrate how the rows of the table 4100 are distributed amongstthe shards 7610-7640 based on the values in the columns A and B. Novalue of the column A is in more than one shard. Any particular value ofthe column A can be in only one shard.

At 6310 of obtaining the results data, the example 6000 receives, fromat least a subset of the database instances of the distributed databaseintermediate results data responsive to at least a portion of thedata-query. That is, the example 6000 receives respective aggregationsof values of the first column for each value of the second column. Eachaggregation of values can be received by the query coordinator from arespective database instance to which a shard of the table isdistributed.

Receiving the intermediate results data can include receiving, from afirst database instance for the first shard, first aggregation valuesindicating, on a per-group basis in accordance with the grouping clause,a respective aggregation value of distinct values of the first column inaccordance with the aggregation clause; and receiving, from a seconddatabase instance for a second shard, second aggregation valuesindicating, on a per-group basis in accordance with the grouping clause,a respective aggregation value of distinct values of the first column inaccordance with the aggregation clause. In an example, the aggregationvalues are cardinality values (i.e., counts of the distinct values).

Referring to the example 7000 of FIG. 7A, using first executioninstruction received from the query coordinator, a first databaseinstance obtains results 7300 from the first shard 7100; and, usingsecond execution instructions received from the query coordinator, asecond database instance obtains results 7400 from the second shard7200. The results 7300 and 7400 count, for each value of the groupingcolumn (i.e., column B) the number of distinct values of the aggregationcolumn (i.e., column A). From the first database instance, the querycoordinator may receive, for example, the aggregation values (e.g., thecardinalities) 1, 2, and 2 for Val_B_1, Val_B_2, and Val_B_3 of B,respectively; and from the second database instance, the querycoordinator may receive, for example, aggregation values (e.g., thecardinalities) 1, 2, and 1 for Val_B_1, Val_B_2, and Val_B_3 of B,respectively. It is noted that first unique values 7310 and secondunique values 7410 are shown for completeness and are not transmitted tothe query coordinator.

Referring to the example 7600 of FIG. 7B, results 7650-7680 are obtainedat database instances from the shards 7610-7640, respectively, based onrespective execution instructions received from the query coordinator.Each of the results 7650-7680 counts, for each value of the groupingcolumn (e.g., column B) the number of distinct values of the aggregationcolumn (e.g., column A). The query coordinator receives, for example,aggregation values (e.g., the cardinalities) 1 and 1 for Val_B_2 andVal_B_3, respectively corresponding to the result 7650; aggregationvalues (e.g., the cardinalities) 1, 1, and 1 for Val_B_1, Val_B_2, andVal_B_3 of B corresponding to the result 7660; and so on. It is notedthat unique values 7685 are shown for completeness and are nottransmitted to the query coordinator.

At 6320 of obtaining the results data, the example 6000 combines theintermediate results to obtain the results data. That is, the example6000 combines the respective aggregations of the values of the firstcolumn to obtain the results data where the results data being anaggregation of the values of the first column for each value of thesecond column.

At 6400, the example 6000 outputs the results data. In an example, theresults data can be used to obtain visualization data or some otheroutput as described herein.

In an example, the example 6000 can include receiving, at a low-latencydata analysis system that includes the distributed database, dataexpressing a usage intent, in response to user input associated with auser; and receiving the data-query can include obtaining the data-queryin response to receiving the data expressing the usage intent, andoutputting the results data can include outputting at least a portion ofthe results data for presentation to the user.

As already described, the aggregation clause can include or can be acounting (i.e., determining a cardinality) of the distinct values of thefirst column. As such, combining the respective aggregations can includequerying at least some of the shards for respective shard-specificdistinct values of the first column (e.g., the values of A that are in athe shard) grouped by shard-specific values of the second column (e.g.,the values of B that are in a the shard), and, for each shard-specificvalue, of the second column, obtaining a respective count of therespective shard-specific distinct values.

In an example, combining the respective aggregations of the values ofthe first column to obtain the results data can include summing, foreach value of the second column, the respective aggregations of thevalues of the first column. Each aggregation of the values of the firstcolumn is a count of the distinct values of the first column. Forexample, and referring to FIG. 7A and FIG. 7B, the example 6000 obtainsthe result data 7500 and result data 7690, respectively.

While the query pseudo-code “select count (distinct A) from T group byB” is used throughout for illustration purposes, it is to be understoodthat other types of queries involving aggregation clauses and groupingclauses are also contemplated. For example, queries that include a“minimum” or a “maximum” aggregation clause (e.g., function) are alsopossible. The minimum function is an aggregate function (e.g., clause)that finds the minimum value in a set, such as “select min (A) from Tgroup by B.” The maximum function is an aggregate function (e.g.,clause) that finds the maximum value in a set, such as “select max (A)from T group by B.” As such, instead of counts, the query coordinatorcan receive the minimum (or maximum) value for each of the values ofgrouping column and, instead of summing, the query coordinator obtains aminimum (or maximum) of all the received minimums (or maximums).

In an example, the aggregation clause can additionally include counting(i.e., determining a cardinality) of distinct values of a third columnof the table that is not included in the sharding criteria of the table.That is, the aggregation clause can include an additional countingclause of distinct values of a third column of the table where the thirdcolumn of the table is not part of the sharding criteria. For example,the data-query can be of the form “select count (distinct A), count(distinct C) from T group by B,” where “count (distinct C)” can be theadditional counting clause and the table T is not sharded on the columnC. In such a case, a respective aggregation of the respectiveaggregations of the values of the first column for the each value of thesecond column further includes the distinct values of the third columnfor the each value of the second column. That is, the distinct values ofthe third column can be included in the aggregations received from thedatabase instances so that the query coordinator can union all thedistinct values to obtain the counts (i.e., cardinalities).

In an example, and as mentioned above, the data-query can include asampling clause. When a sampling clause is present, the example 6000obtains aggregations (e.g., unique counts) for only sampled groups(i.e., not for all groups) of the grouping column. In an example, thesampling clause can be the “limit” clause. For example, the data-querycan include “select count (distinct A) from T group by B limit 10,”which returns distinct counts for only 10 values of the column B). Othersampling clauses are possible. Any clause that performs filtering on thesecond column may be considered to be a sampling clause. The samplingclause can be a combination of clauses that result in computing uniqueaggregations for (i.e., returning results data for) a subset of thegroups of the second column.

When data are sharded by a first column (e.g., column A), the samevalues of A may be in only one shard. The number of distinct elements ofthe first column can be computed for each shard for each value of thesecond column (e.g., column B). The count can be transmitted for eachvalue of the second column to one or more database instances, such as aquery coordinator. The counts can then be summed from different databaseinstances for each value of the second column.

FIG. 8 is a flowchart of an example 8000 of query planning in adistributed database according to implementations of this disclosure.The distributed database includes a table that is partitioned intoshards according to a sharding criterion. The shards can be distributedto database instances of the distributed database. The example 8000 canbe used to derive and execute a query plan for a data-query thatincludes a first “distinct count” clause on a first column of the tableand a “group by” clause on least a second column of the table. In anexample, the data-query can include a sampling clause.

A “distinct count” clause in this context broadly means any query clauseor function whereby the specific values of the aggregation column in theshards are not required by the query coordinator to obtain results data.For example, a “distinct count” clause encompasses a distinct countclause, a minimum function clause, or a maximum function clause asdescribed herein.

The example 8000 may be implemented by a distributed database, such asthe distributed in-memory database 3300 of FIG. 3 . For example, theexample 8000 may be implemented by a database instance, such asin-memory database instance as described herein. The example 8000 can beimplemented by an internal database analysis portion, such as theinternal database analysis portion 2200 of FIG. 2 , or an aspectthereof, such as one or more of the servers 2220, 2240, 2260, and 2280.For example, the example 8000 may be implemented using the computingdevice 1000 of FIG. 1 . The example 8000 can be implemented fully orpartially by a query coordinator. The example 8000 can be implementedfully or partially by a database instance that is not a querycoordinator.

At 8100, the example 8000 receives a data-query at a query coordinator.The query coordinator can be identified as described herein. Thedata-query includes a first “distinct count” clause on a first column ofthe table and a “group by” clause on least a second column of the table.The table is partitioned into the shards according to a shardingcriterion;

At 8200, the example 8000 formulates a query plan. The query plan can beformulated based on a determination that the sharding criterion includesthe first column. In an example, the table can be sharded on the firstcolumn. In an example, the table can be sharded on the first column andthe second column. Formulating the query plan can include steps8210-8230. The query plan can be formulated by the query coordinator.The query plan is such that distinct values of the first column are nottransmitted from at least some of the database instances to the querycoordinator.

For illustration purposes, formulating the query plan is described withreference to FIG. 9A and FIG. 9B. FIG. 9A is a diagram of an example ofa query plan 9000 for a data-query for aggregations together withgroupings of data in a distributed database according to implementationsof this disclosure. FIG. 9B is a diagram of an example of a query plan9500 for a data-query for aggregations together with groupings of datain a distributed database according to implementations of thisdisclosure. The query plan of the query plan 9500 can be used when thedata-query includes a second “distinct count” clause on third column andthe sharding criterion does not include the third column.

The query plan 9000 of FIG. 9A includes that the table T is partitionedinto at least two shards: shards 9100A (i.e., region T.R0) and 9100B(i.e., region T.R1); and the query plan 9500 of FIG. 9B includes thatthe table T is partitioned into at least two shards: shards 9600A (i.e.,region T.R0) and 9600B (i.e., region T.R1).

At 8210, the query plan includes instructions for converting, at atleast some of the database instances, distinct values of the firstcolumn grouped by values of the second column into a count of thedistinct values grouped by the values of the second column to obtainrespective intermediate results. The instructions for converting aretransmitted to the at least some of the database instances forexecution. The at least some of the database instances can include thequery coordinator.

In the query plan 9000, the query plan includes that, at at least someof the database instances, a grouping 9200 is to be performed wherebythe shard is queried to obtain the distinct values of the first column(e.g., column A) grouped by the second column (e.g., column B). Eachgroup of distinct values can be stored in a respective container X. Thequery plan includes a transformation 9250 that converts (e.g., causesthe database instance to convert) the respective containers X into therespective counts (e.g., Y=CONTAINER SIZE(X)), which are the sizes of(i.e., the number of elements in) the respective containers X.

In the query plan 9500, the query plan includes that, at at least someof the database instances, a grouping 9700 is to be performed wherebythe shard is queried to obtain the distinct values of the first column(e.g., column A) and the third column (e.g., column C) grouped by thesecond column. Each group of distinct values of the first column are tobe stored in a respective container X. The query plan includes atransformation 9750 that converts the respective containers X into therespective counts (e.g., Y=CONTAINER SIZE(X)), which are the sizes of(i.e., the number of elements in) the respective containers X. It isnoted that the transformation 9750 retains the distinct values of thethird column (e.g., DISTINCT(C)).

At 8220, the query plan includes instructions for receiving, at thequery coordinator, the respective intermediate results from at least asubset of the at least some of the database instances. A shard may notinclude values of the second column. As such, no transformation may bereceived corresponding to the shard. Alternatively or equivalently, anempty transformation, a null transformation, or an indication thereofmay be received at the query coordinator.

In the query plan 9000, the query coordinator concatenates (e.g.,includes execution instructions to concatenate) the received respectivetransformations at a concatenation 9300. In the query plan 9500, thequery coordinator concatenates (e.g., performs instructions toconcatenate) the received respective transformations at a concatenation9800.

At 8230, the query plan includes instructions for concatenating therespective intermediate results using a summing operation to obtain the“distinct count” of the first column grouped by the second column. Thatis, the execution plan includes execution instructions for the summingoperation. In the query plan 9000, a grouping 9400 can be performed bythe query coordinator by adding (e.g., SUM(Y)), for each value of thesecond column the received counts. The results data are obtained at atransformation 9450. The transformation 9450 indicates that for eachvalue of B, a respective count Z is obtained. As can be appreciated, theaggregation operation used depends on the aggregation clause. As such,wherein the SUM(Y) operation is used in the case that the aggregationclause is a “count (distinct A),” a MIN(Y) or MAX(Y) can be use when theaggregation clause is “min(A)” or “max(A),” respectively.

In the query plan 9500, a grouping 9900 can be performed by the querycoordinator by adding (e.g., SUM(Y)), for each value of the secondcolumn the received counts. As mentioned, the aggregation operation useddepends on the aggregation clause. The query coordinator can alsoobtain, for each value of the second column, all the distinct values ofthe third column by aggregating (e.g., using a union operation asdescribed above) grouped by the second column. The results data areobtained at a transformation 9950. As such, the query plan can includerespective instructions for including, at the least some of the databaseinstances, respective distinct values of the third column grouped by thefirst column, and instructions for performing, for each distinct valueof first column, a union aggregation of all the respective distinctvalues of the third column grouped by the first column received from thedatabase instances.

At 8300, the example 8000 executes the query plan to obtain the resultsdata. Executing the query plan includes transmitting instructions of thequery plan to database instances for execution. At 8400, the example8000 includes outputting the results data.

In an example, the example 8000 can include determining whether thetable is sharded on the grouping column first. If the table is shardedon the grouping column, then the data-query can be executed as describedwith respect to FIG. 5 . If the table is not sharded on the groupingcolumn, the example 8000 can combine one aggregate clause with thegrouping clause to determine whether the combination can meet thesharding criteria.

FIG. 10 is a flowchart of an example 10000 of querying a distributeddatabase according to implementations of this disclosure. The example10000 can be implemented by a database instance to query a shard of atable of a distributed database in response to receiving a data-query.The database instance can query the shard in response to executioninstructions of a query plan received from a query coordinator.

The table includes a first column that is an aggregation column of thedata-query. The table includes a second column that is a grouping columnof the data-query. In an example, the aggregation clause of thedata-query includes counting (i.e., determining the cardinality of)distinct values of the first column for each value of the second column.

The sharding of the table is such that any one value of the first columnis in only one shard. That is, values of the first column in the shardaccording to the sharding criterion are not in any other shard of thetable.

At 10100, the example 10000 extracts, from the shard of the table of thedistributed database, respective distinct values of a first column ofthe table for each value of a second column of the table. In an example,the respective distinct values of a first column can be extracted asdescribed with respect to 9200 of FIG. 9A.

At 10200, the example 10000 obtains an intermediate result bydetermining a respective number of the respective distinct values forthe each value of the second column of the table. In an example, theexample 10000 obtains the intermediate result as described with respectto 9250 of FIG. 9A.

At 10300, the example 10000 transmits the intermediate result to a querycoordinator. As described above, the query coordinator can combineseveral partial results from more than one database instance to obtainresults data.

In an example, the data-query can further include an aggregation clauseon a third column where the sharding criterion omits (e.g., does notinclude) the third column. That is, the table is not sharded on thethird column. In an example, the intermediate results further includedistinct values of the third column grouped by the distinct values forthe each value of the second column of the table.

While aggregation operations in a distributed database are describedwith respect to a distributed database, a table of a database, andcolumns of the table, it can be appreciated that the techniquesdescribed herein can be used with any data that is partitioned such thatprocessing can be performed, such as in parallel, one a partition basisand each partition includes unique values of a first aspect (e.g.,property, attribute, etc.) of the data and the processing includesobtaining aggregations (e.g., distinct counts) of the first aspectgrouped by a second aspect of the data.

As used herein, the terminology “computer” or “computing device”includes any unit, or combination of units, capable of performing anymethod, or any portion or portions thereof, disclosed herein.

As used herein, the terminology “processor” indicates one or moreprocessors, such as one or more special purpose processors, one or moredigital signal processors, one or more microprocessors, one or morecontrollers, one or more microcontrollers, one or more applicationprocessors, one or more central processing units (CPU)s, one or moregraphics processing units (GPU)s, one or more digital signal processors(DSP)s, one or more application specific integrated circuits (ASIC)s,one or more application specific standard products, one or more fieldprogrammable gate arrays, any other type or combination of integratedcircuits, one or more state machines, or any combination thereof.

As used herein, the terminology “memory” indicates any computer-usableor computer-readable medium or device that can tangibly contain, store,communicate, or transport any signal or information that may be used byor in connection with any processor. For example, a memory may be one ormore read only memories (ROM), one or more random access memories (RAM),one or more registers, low power double data rate (LPDDR) memories, oneor more cache memories, one or more semiconductor memory devices, one ormore magnetic media, one or more optical media, one or moremagneto-optical media, or any combination thereof.

As used herein, the terminology “instructions” may include directions orexpressions for performing any method, or any portion or portionsthereof, disclosed herein, and may be realized in hardware, software, orany combination thereof. For example, instructions may be implemented asinformation, such as a computer program, stored in memory that may beexecuted by a processor to perform any of the respective methods,algorithms, aspects, or combinations thereof, as described herein.Instructions, or a portion thereof, may be implemented as a specialpurpose processor, or circuitry, that may include specialized hardwarefor carrying out any of the methods, algorithms, aspects, orcombinations thereof, as described herein. In some implementations,portions of the instructions may be distributed across multipleprocessors on a single device, on multiple devices, which maycommunicate directly or across a network such as a local area network, awide area network, the Internet, or a combination thereof.

As used herein, the terminology “determine,” “identify,” “obtain,” and“form” or any variations thereof, includes selecting, ascertaining,computing, looking up, receiving, determining, establishing, obtaining,or otherwise identifying or determining in any manner whatsoever usingone or more of the devices and methods shown and described herein.

As used herein, the term “computing device” includes any unit, orcombination of units, capable of performing any method, or any portionor portions thereof, disclosed herein.

As used herein, the terminology “example,” “embodiment,”“implementation,” “aspect,” “feature,” or “element” indicates serving asan example, instance, or illustration. Unless expressly indicated, anyexample, embodiment, implementation, aspect, feature, or element isindependent of each other example, embodiment, implementation, aspect,feature, or element and may be used in combination with any otherexample, embodiment, implementation, aspect, feature, or element.

As used herein, the terminology “or” is intended to mean an inclusive“or” rather than an exclusive “or.” That is, unless specified otherwise,or clear from context, “X includes A or B” is intended to indicate anyof the natural inclusive permutations. That is, if X includes A; Xincludes B; or X includes both A and B, then “X includes A or B” issatisfied under any of the foregoing instances. In addition, thearticles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from the context to be directed to asingular form.

Further, for simplicity of explanation, although the figures anddescriptions herein may include sequences or series of steps or stages,elements of the methods disclosed herein may occur in various orders orconcurrently. Additionally, elements of the methods disclosed herein mayoccur with other elements not explicitly presented and described herein.Furthermore, not all elements of the methods described herein may berequired to implement a method in accordance with this disclosure.Although aspects, features, and elements are described herein inparticular combinations, each aspect, feature, or element may be usedindependently or in various combinations with or without other aspects,features, and elements.

Although some embodiments herein refer to methods, it will beappreciated by one skilled in the art that they may also be embodied asa system or computer program product. Accordingly, aspects of thepresent invention may take the form of an entirely hardware embodiment,an entirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “processor,”“device,” or “system.” Furthermore, aspects of the present invention maytake the form of a computer program product embodied in one or morecomputer readable mediums having computer readable program code embodiedthereon. Any combination of one or more computer readable mediums may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium include the following: an electrical connection havingone or more wires, a portable computer diskette, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), an optical fiber, a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the context of this document, a computer readable storage medium maybe any tangible medium that can contain or store a program for use by orin connection with an instruction execution system, apparatus, ordevice.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to CDs, DVDs,wireless, wireline, optical fiber cable, RF, etc., or any suitablecombination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Attributes may comprise any data characteristic, category, content, etc.that in one example may be non-quantifiable or non-numeric. Measures maycomprise quantifiable numeric values such as sizes, amounts, degrees,etc. For example, a first column containing the names of states may beconsidered an attribute column and a second column containing thenumbers of orders received for the different states may be considered ameasure column.

Aspects of the present embodiments are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a computer, such as a special purposecomputer, or other programmable data processing apparatus to produce amachine, such that the instructions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmeans for implementing the functions/acts specified in the flowchartand/or block diagram block or blocks. These computer programinstructions may also be stored in a computer readable medium that candirect a computer, other programmable data processing apparatus, orother devices to function in a particular manner, such that theinstructions stored in the computer readable medium produce an articleof manufacture including instructions which implement the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer program instructions may also be loaded onto a computer, otherprogrammable data processing apparatus, or other devices to cause aseries of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. The flowcharts and block diagrams in thefigures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present invention. Inthis regard, each block in the flowchart or block diagrams may representa module, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It should also be noted that, in some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

While the disclosure has been described in connection with certainembodiments, it is to be understood that the disclosure is not to belimited to the disclosed embodiments but, on the contrary, is intendedto cover various modifications and equivalent arrangements includedwithin the scope of the appended claims, which scope is to be accordedthe broadest interpretation so as to encompass all such modificationsand equivalent structures as is permitted under the law.

What is claimed is:
 1. A method for querying a distributed database,comprising: receiving a data-query at the distributed database, whereinthe distributed database comprises a table, the table comprises a firstcolumn and a second column, the table is partitioned into shardsaccording to a sharding criterion, wherein the sharding criterionindicates the first column, such that a first shard includes one or morerows of the table having a first value of the first column and a secondshard omits a row of the table having the first value of the firstcolumn, the shards are distributed to database instances of thedistributed database, and the data-query comprises an aggregation clauseon the first column and a grouping clause on the second column;identifying a query coordinator for processing the data-query; obtainingresults data responsive to the data-query, wherein obtaining the resultsdata comprises: receiving, by the query coordinator and from at least asubset of the database instances of the distributed database,intermediate results data responsive to at least a portion of thedata-query, wherein receiving the intermediate results data includes:receiving, from a first database instance for a first shard, firstaggregation values indicating, on a per-group basis in accordance withthe grouping clause, a respective aggregation value of distinct valuesof the first column in accordance with the aggregation clause, andreceiving, from a second database instance for a second shard, secondaggregation values indicating, on a per-group basis in accordance withthe grouping clause, a respective aggregation value of distinct valuesof the first column in accordance with the aggregation clause; andcombining, by the query coordinator, the intermediate results to obtainthe results data; and outputting the results data.
 2. The method ofclaim 1, wherein the aggregation clause comprises determining acardinality of distinct values of the first column.
 3. The method ofclaim 2, wherein combining, by the query coordinator, the intermediateresults to obtain the results data comprises: querying at least some ofthe shards for respective shard-specific distinct values of the firstcolumn grouped by shard-specific values of the second column; and foreach shard-specific value of the second column, obtaining a respectivecount of the respective shard-specific distinct values.
 4. The method ofclaim 1, further comprising: receiving, at a low-latency data analysissystem that includes the distributed database, data expressing a usageintent, in response to user input associated with a user, whereinreceiving the data-query comprises: obtaining the data-query in responseto receiving the data expressing the usage intent; and whereinoutputting the results data comprises: outputting at least a portion ofthe results data for presentation to the user.
 5. The method of claim 1,wherein the sharding criterion consists of the first column of thetable.
 6. The method of claim 1, wherein the sharding criterioncomprises the first column of the table and the second column of thetable.
 7. The method of claim 6, wherein the sharding criterioncomprises sharding on the first column followed by sharding on thesecond column.
 8. The method of claim 1, wherein the aggregation clausecomprises at least one of a minimum clause or a maximum clause.
 9. Themethod of claim 1, wherein the aggregation clause further comprisesdetermining a cardinality of distinct values of a third column of thetable, wherein the sharding criterion does not include the third columnof the table, and wherein an intermediate result of the intermediateresults further comprises distinct values of the third column for theeach value of the second column.
 10. The method of claim 1, wherein thedata-query further comprises a sampling clause.
 11. A device forquerying a distributed database, comprising: a memory; and a processor,the processor configured to execute instructions stored in the memoryto: receive a data-query at the distributed database, wherein thedistributed database comprises a table, the table comprises a firstcolumn and a second column, the table is partitioned into shardsaccording to a sharding criterion, wherein the sharding criterionindicates the first column, such that a first shard includes one or morerows of the table having a first value of the first column and a secondshard omits a row of the table having the first value of the firstcolumn, the shards are distributed to database instances of thedistributed database, and the data-query comprises an aggregation clauseon the first column and a grouping clause on the second column; identifya query coordinator for processing the data-query; obtain results dataresponsive to the data-query, wherein to obtain the results datacomprises to: receive, by the query coordinator and from at least asubset of the database instances of the distributed database,intermediate results data responsive to at least a portion of thedata-query, wherein to receive the intermediate results data includesto: receive, from a first database instance for a first shard, firstaggregation values indicating, on a per-group basis in accordance withthe grouping clause, a respective aggregation value of distinct valuesof the first column in accordance with the aggregation clause, andreceive, from a second database instance for a second shard, secondaggregation values indicating, on a per-group basis in accordance withthe grouping clause, a respective aggregation value of distinct valuesof the first column in accordance with the aggregation clause; andcombine, by the query coordinator, the intermediate results to obtainthe results data; and output the results data.
 12. The device of claim11, wherein the aggregation clause comprises determining a cardinalityof distinct values of the first column.
 13. The device of claim 12,wherein to combine, by the query coordinator, the intermediate resultsto obtain the results data comprises to: query at least some of theshards for respective shard-specific distinct values of the first columngrouped by shard-specific values of the second column; and for eachshard-specific value of the second column, obtain a respective count ofthe respective shard-specific distinct values.
 14. The device of claim11, wherein the processor is further configured to execute instructionsto: receive, at a low-latency data analysis system that includes thedistributed database, data expressing a usage intent, in response touser input associated with a user, wherein to receive the data-querycomprises to: obtain the data-query in response to receiving the dataexpressing the usage intent; and wherein to output the results datacomprises to: output at least a portion of the results data forpresentation to the user.
 15. The device of claim 11, wherein thesharding criterion consists of the first column of the table.
 16. Thedevice of claim 11, wherein the sharding criterion comprises the firstcolumn of the table and the second column of the table.
 17. The deviceof claim 16, wherein the sharding criterion comprises sharding on thefirst column followed by sharding on the second column.
 18. The deviceof claim 11, wherein the aggregation clause comprises at least one of aminimum clause or a maximum clause.
 19. The device of claim 11, whereinthe aggregation clause further comprises determining a cardinality ofdistinct values of a third column of the table, wherein the shardingcriterion does not include the third column of the table, and wherein anintermediate result of the intermediate results further comprisesdistinct values of the third column for the each value of the secondcolumn.
 20. A non-transitory computer readable medium storinginstructions operable to cause one or more processors to performoperations for querying a distributed database, comprising: receiving adata-query at the distributed database, wherein the distributed databasecomprises a table, the table comprises a first column and a secondcolumn, the table is partitioned into shards according to a shardingcriterion, wherein the sharding criterion indicates the first column,such that a first shard includes one or more rows of the table having afirst value of the first column and a second shard omits a row of thetable having the first value of the first column, the shards aredistributed to database instances of the distributed database, and thedata-query comprises an aggregation clause on the first column and agrouping clause on the second column; identifying a query coordinatorfor processing the data-query; obtaining results data responsive to thedata-query, wherein obtaining the results data comprises: receiving, bythe query coordinator and from at least a subset of the databaseinstances of the distributed database, intermediate results dataresponsive to at least a portion of the data-query, wherein receivingthe intermediate results data includes: receiving, from a first databaseinstance for a first shard, first aggregation values indicating, on aper-group basis in accordance with the grouping clause, a respectiveaggregation value of distinct values of the first column in accordancewith the aggregation clause, and receiving, from a second databaseinstance for a second shard, second aggregation values indicating, on aper-group basis in accordance with the grouping clause, a respectiveaggregation value of distinct values of the first column in accordancewith the aggregation clause; and combining, by the query coordinator,the intermediate results to obtain the results data; and outputting theresults data.